@inproceedings{Lenz2026NumbersDontLie,
  title = {Numbers {{Don}}'t {{Lie}}: {{Hybrid Extraction}} and~{{Validation}} of~{{Quantitative Statements}} in~{{Arguments}} with~{{Semi-structured Information}}},
  shorttitle = {Numbers {{Don}}'t {{Lie}}},
  booktitle = {{{KI}} 2025: {{Advances}} in {{Artificial Intelligence}}},
  author = {Lenz, Mirko and Dumani, Lorik and Schenkel, Ralf and Bergmann, Ralph},
  editor = {Braun, Tanya and Paa{\ss}en, Benjamin and Stolzenburg, Frieder},
  year = {2026},
  series = {Lecture {{Notes}} in {{Computer Science}}},
  volume = {15956},
  pages = {77--90},
  publisher = {Springer Nature Switzerland},
  address = {Cham},
  doi = {10.1007/978-3-032-02813-6_6},
  abstract = {Evidence in arguments may be stated in various forms, including quantitative statements (i.e., numerical relations between entities). This measurable information can be validated against reliable sources like Wikipedia to combat the spread of misinformation. In this paper, we propose a four-step pipeline that combines rule-based techniques with prompting strategies for generative language models in a hybrid fashion. We use regular expressions to identify candidates in claim-premise structures, extract statements using GPT-4o, augment the data with tables from Wikipedia, and validate statements through retrieval-augmented generation (RAG). The pipeline is evaluated on two existing argumentation corpora and the generated dataset is manually annotated to assess the quality of our predictions, showing promising results for extraction and mixed results for validation. Our code and data are available to foster further research in this area.},
  isbn = {978-3-032-02813-6},
  langid = {english},
  url = {https://www.wi2.uni-trier.de/shared/publications/Lenz2026NumbersDontLie.pdf}
}

Evidence in arguments may be stated in various forms, including quantitative statements (i.e., numerical relations between entities). This measurable information can be validated against reliable sources like Wikipedia to combat the spread of misinformation. In this paper, we propose a four-step pipeline that combines rule-based techniques with prompting strategies for generative language models in a hybrid fashion. We use regular expressions to identify candidates in claim-premise structures, extract statements using GPT-4o, augment the data with tables from Wikipedia, and validate statements through retrieval-augmented generation (RAG). The pipeline is evaluated on two existing argumentation corpora and the generated dataset is manually annotated to assess the quality of our predictions, showing promising results for extraction and mixed results for validation. Our code and data are available to foster further research in this area.

@inproceedings{Imenkamp2025YAPNE,
  author    = {Christian Imenkamp and Joscha Gr{\"u}ger and Martin Kuhn and Christoph Matheja and Andrey Rivkin and Agnes Koschmider},
  title     = {YAPNE: A Tool for Modeling and Automated Verification of Data Petri Nets},
  booktitle = {Proceedings of the ICPM Doctoral Consortium and Demo Track 2025 co-located with the 7th International Conference on Process Mining (ICPM 2025)},
  editor    = {Michael Arias Chaves and Henrik Leopold and Adriana Marotta and Manuel Resinas and Francesca Zerbato},
  year      = {2025},
  month     = oct,
  series    = {CEUR Workshop Proceedings},
  volume    = {4088},
  publisher = {CEUR-WS.org},
  issn      = {1613-0073},
  url       = {https://ceur-ws.org/Vol-4088/paper_250.pdf},
  urn       = {urn:nbn:de:0074-4088-x},
  note      = {Demo Track, Montevideo, Uruguay}
}

@article{SchultheisBGMBSA2025,
  author       = {Schultheis, Alexander and Bertrand, Yannis and Grüger, Joscha and Malburg, Lukas and Bergmann, Ralph and {Serral Asensio}, Estefanía},
  title        = {{Case-based Data Quality Management for IoT Logs: A Case Study Focusing on Detection of Data Quality Issues}},
  journal      = {IoT},
  year 		   = {2025},
  volume = {6},
  number = {4},
  article-number = {63},
  doi = {10.3390/iot6040063},
  abstract = {Smart manufacturing applications increasingly rely on time-series data from Industrial IoT sensors, yet these data streams often contain data quality issues (DQIs) that affect analysis and disrupt production. While traditional Machine Learning methods are difficult to apply due to the small amount of data available, the knowledge-based approach of Case-Based Reasoning (CBR) offers a way to reuse previously gained experience. We introduce the first end-to-end Case-Based Reasoning (CBR) framework that both detects and remedies DQIs in near real time, even when only a handful of annotated fault instances are available. Our solution encodes expert experience in the four CBR knowledge containers: (i) a vocabulary that represents sensor streams and their context in the DataStream format; (ii) a case base populated with fault-annotated event logs; (iii) tailored similarity measures—including a weighted Dynamic Time Warping variant and structure-aware list mapping—that isolate the signatures of missing-value, missing-sensor, and time-shift errors; and (iv) lightweight adaptation rules that recommend concrete repair actions or, where appropriate, invoke automated imputation and alignment routines. A case study is used to examine and present the suitability of the approach for a specific application domain. Although the case study demonstrates only limited capabilities in identifying Data Quality Issues (DQIs), we aim to support transparent evaluation and future research by publishing (1) a prototype of the Case-Based Reasoning (CBR) system and (2) a publicly accessible, meticulously annotated sensor-log benchmark. Together, these resources provide a reproducible baseline and a modular foundation for advancing similarity metrics, expanding the DQI taxonomy, and enabling knowledge-intensive reasoning in IoT data quality management.},
  keywords = {Time Series Data, Industrial Internet of Things, Data Quality Issues, Temporal Case-Based Reasoning}
}

Smart manufacturing applications increasingly rely on time-series data from Industrial IoT sensors, yet these data streams often contain data quality issues (DQIs) that affect analysis and disrupt production. While traditional Machine Learning methods are difficult to apply due to the small amount of data available, the knowledge-based approach of Case-Based Reasoning (CBR) offers a way to reuse previously gained experience. We introduce the first end-to-end Case-Based Reasoning (CBR) framework that both detects and remedies DQIs in near real time, even when only a handful of annotated fault instances are available. Our solution encodes expert experience in the four CBR knowledge containers: (i) a vocabulary that represents sensor streams and their context in the DataStream format; (ii) a case base populated with fault-annotated event logs; (iii) tailored similarity measures—including a weighted Dynamic Time Warping variant and structure-aware list mapping—that isolate the signatures of missing-value, missing-sensor, and time-shift errors; and (iv) lightweight adaptation rules that recommend concrete repair actions or, where appropriate, invoke automated imputation and alignment routines. A case study is used to examine and present the suitability of the approach for a specific application domain. Although the case study demonstrates only limited capabilities in identifying Data Quality Issues (DQIs), we aim to support transparent evaluation and future research by publishing (1) a prototype of the Case-Based Reasoning (CBR) system and (2) a publicly accessible, meticulously annotated sensor-log benchmark. Together, these resources provide a reproducible baseline and a modular foundation for advancing similarity metrics, expanding the DQI taxonomy, and enabling knowledge-intensive reasoning in IoT data quality management.

@phdthesis{Malburg_PhDThesis_2025,
    abstract = {The manufacturing industry is confronted with challenges caused by shorter innovation and product lifecycles, increasing complexity of production due to individualization and customization, and production disturbances of various kinds. 
To address these challenges, more autonomous and intelligent production systems are required, enabling the manufacturing industry to react more flexibly to these changing conditions. 
One goal of the Fourth Industrial Revolution, also known as Industry 4.0 (I4.0), is to enhance the digitalization and connectivity of high-level information systems with low-level production processes.
This connectivity is enabled by the fact that an increasing number of devices and machines are connected to the Internet, known as the Internet of Things (IoT).
According to this, IoT is a key technology for realizing Cyber-Physical Systems (CPSs), linking the physical world, i.e., devices and machines, with the digital world.
In the manufacturing industry, Cyber-Physical Production Systems (CPPSs) as a special kind of CPSs are applied to optimize production processes and to increase efficiency and flexibility in manufacturing. 
Although CPPSs are already used in industry and there is a growing amount of research in this field, a bidirectional coupling between high-level information systems and low-level production processes is often still missing.
Consequently, the IoT sensor data generated during production cannot be fully utilized to monitor low-level production processes, which in turn makes it more difficult for high-level information systems to detect emerging situations at an early stage and to perform real-time adaptations.
The approaches for enabling flexibility and adaptability of processes in CPPSs are still in their infancy and other available approaches do not adequately consider the specific characteristics of CPPSs such as unanticipated behavior or physical errors.
Consequently, current production processes are often still too rigid, which makes it difficult to react to changing environmental conditions or disturbances in real-time and in an automated way.

This thesis aims to contribute to the aforementioned problems by developing an approach for adaptive workflow management for CPPSs by using Artificial Intelligence (AI) techniques. 
For this purpose, a systems architecture is developed that enables the bidirectional coupling between high-level information systems and low-level processes.
Based on this coupling, Business Process Management (BPM) methods are used to model production processes on an abstract level and in a more structured way, allowing the flexible composition and execution of low-level production processes.
Moreover, methods from BPM enable the utilization of IoT sensor data for process monitoring and analysis, building the basis for detecting emerging situations and disturbances in production processes earlier.
The presented adaptive workflow management approach enables resolving such situations by ad hoc adaptations of production processes in near real-time.
This is achieved by combining search-intensive AI planning and knowledge-intensive Case-Based Reasoning (CBR).
One main advantage of this combined approach is that already experienced problem situations in the form of performed ad hoc adaptations (called cases) can be reused to solve new upcoming similar problems. 
In addition, the search-intensive AI planning solves problems that have not yet been experienced as cases.
In this way, the combined approach helps to limit both the high knowledge acquisition and modeling efforts and the high computational complexity compared to using search-intensive AI planning solely.
At the same time, it also increases problem-solving competence in situations where CBR alone cannot yet solve these problems.
Finally, the approach enables incremental self-learning and, thus, improves the problem-solving competence continuously.
In addition, the use of CBR enables the integration of domain experts and their expertise into the combined approach.

The experimental evaluations indicate the suitability of the proposed approach for performing ad hoc adaptations in CPPSs.
For this purpose, a learning factory has been used as a test bed to simulate production processes of a real manufacturing environment.
Using such learning factories, the characteristics of real-world production environments are considered, and at the same time, such learning factories allow a more cost-effective and easier realization of I4.0 research.
For evaluation, the production processes have been modeled and executed in the learning factory.
In addition, failure situations have been generated to test whether these failures can be detected with enhanced monitoring techniques and to adapt production processes using the proposed adaptive workflow management approach.},
    year = {2025},
    title = {{Adaptive Workflow Management for Cyber-Physical Systems - A Hybrid Approach Combining Experience-Based Learning and Automated Planning for Industry 4.0}},
    school = {Universität Trier},
    author = {Lukas Malburg},
    keywords = {Adaptive Workflow Management, Cyber-Physical Production Systems, Internet of Things, Case-Based Reasoning, Automated Planning}
}

The manufacturing industry is confronted with challenges caused by shorter innovation and product lifecycles, increasing complexity of production due to individualization and customization, and production disturbances of various kinds. To address these challenges, more autonomous and intelligent production systems are required, enabling the manufacturing industry to react more flexibly to these changing conditions. One goal of the Fourth Industrial Revolution, also known as Industry 4.0 (I4.0), is to enhance the digitalization and connectivity of high-level information systems with low-level production processes. This connectivity is enabled by the fact that an increasing number of devices and machines are connected to the Internet, known as the Internet of Things (IoT). According to this, IoT is a key technology for realizing Cyber-Physical Systems (CPSs), linking the physical world, i.e., devices and machines, with the digital world. In the manufacturing industry, Cyber-Physical Production Systems (CPPSs) as a special kind of CPSs are applied to optimize production processes and to increase efficiency and flexibility in manufacturing. Although CPPSs are already used in industry and there is a growing amount of research in this field, a bidirectional coupling between high-level information systems and low-level production processes is often still missing. Consequently, the IoT sensor data generated during production cannot be fully utilized to monitor low-level production processes, which in turn makes it more difficult for high-level information systems to detect emerging situations at an early stage and to perform real-time adaptations. The approaches for enabling flexibility and adaptability of processes in CPPSs are still in their infancy and other available approaches do not adequately consider the specific characteristics of CPPSs such as unanticipated behavior or physical errors. Consequently, current production processes are often still too rigid, which makes it difficult to react to changing environmental conditions or disturbances in real-time and in an automated way. This thesis aims to contribute to the aforementioned problems by developing an approach for adaptive workflow management for CPPSs by using Artificial Intelligence (AI) techniques. For this purpose, a systems architecture is developed that enables the bidirectional coupling between high-level information systems and low-level processes. Based on this coupling, Business Process Management (BPM) methods are used to model production processes on an abstract level and in a more structured way, allowing the flexible composition and execution of low-level production processes. Moreover, methods from BPM enable the utilization of IoT sensor data for process monitoring and analysis, building the basis for detecting emerging situations and disturbances in production processes earlier. The presented adaptive workflow management approach enables resolving such situations by ad hoc adaptations of production processes in near real-time. This is achieved by combining search-intensive AI planning and knowledge-intensive Case-Based Reasoning (CBR). One main advantage of this combined approach is that already experienced problem situations in the form of performed ad hoc adaptations (called cases) can be reused to solve new upcoming similar problems. In addition, the search-intensive AI planning solves problems that have not yet been experienced as cases. In this way, the combined approach helps to limit both the high knowledge acquisition and modeling efforts and the high computational complexity compared to using search-intensive AI planning solely. At the same time, it also increases problem-solving competence in situations where CBR alone cannot yet solve these problems. Finally, the approach enables incremental self-learning and, thus, improves the problem-solving competence continuously. In addition, the use of CBR enables the integration of domain experts and their expertise into the combined approach. The experimental evaluations indicate the suitability of the proposed approach for performing ad hoc adaptations in CPPSs. For this purpose, a learning factory has been used as a test bed to simulate production processes of a real manufacturing environment. Using such learning factories, the characteristics of real-world production environments are considered, and at the same time, such learning factories allow a more cost-effective and easier realization of I4.0 research. For evaluation, the production processes have been modeled and executed in the learning factory. In addition, failure situations have been generated to test whether these failures can be detected with enhanced monitoring techniques and to adapt production processes using the proposed adaptive workflow management approach.

@misc{BachEtAl_CBRMeetsLLMs_2025,
    abstract = {In recent years, the surge of Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs), has led to a significant increase in the use of hybrid systems, which combine the strengths of different Artificial Intelligence (AI) paradigms to achieve better performance and efficiency. Although LLMs demonstrate remarkable effectiveness across numerous tasks due to their flexibility and general knowledge, they often face challenges related to accuracy, explainability, and their limited memory. CaseBased Reasoning (CBR), on the other hand, excels by recalling past experiences and using them to solve new problems, making it particularly well suited for tasks that require contextual understanding and decisionmaking. However, CBR systems suffer from issues such as the acquisition of various kinds of knowledge and the application of methods during the 4R cycle. In this paper, we identify several challenges plaguing LLMs and CBR systems and propose opportunities to combine the strengths of both methodologies to address these challenges. In addition, we outline future research directions for the community to explore.},
    month = {3},
    year = {2025},
    title = {{Case-Based Reasoning Meets Large Language Models: A Research Manifesto For Open Challenges and Research Directions}},
    note = {working paper or preprint},
    author = {Bach, Kerstin and Bergmann, Ralph and Brand, Florian and Caro-Mart{\'i}nez, Marta and Eisenstadt, Viktor and W. Floyd, Michael and Jayawardena, Lasal and Leake, David and Lenz, Mirko and Malburg, Lukas and H. M{\'e}nager, David and Minor, Mirjam and Schack, Brian and Watson, Ian and Wilkerson, Kaitlynne and Wiratunga, Nirmalie},
	keywords = {Large Language Models, Case-Based Reasoning, Large Reasoning Models, Retrieval Augmented Generation, Challenges and Opportunities}
}

In recent years, the surge of Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs), has led to a significant increase in the use of hybrid systems, which combine the strengths of different Artificial Intelligence (AI) paradigms to achieve better performance and efficiency. Although LLMs demonstrate remarkable effectiveness across numerous tasks due to their flexibility and general knowledge, they often face challenges related to accuracy, explainability, and their limited memory. CaseBased Reasoning (CBR), on the other hand, excels by recalling past experiences and using them to solve new problems, making it particularly well suited for tasks that require contextual understanding and decisionmaking. However, CBR systems suffer from issues such as the acquisition of various kinds of knowledge and the application of methods during the 4R cycle. In this paper, we identify several challenges plaguing LLMs and CBR systems and propose opportunities to combine the strengths of both methodologies to address these challenges. In addition, we outline future research directions for the community to explore.

@inproceedings{Bartz2025RetrievingArgumentGraphs,
  title = {Retrieving {{Argument Graphs Using Vision Transformers}}},
  booktitle = {Proceedings of the 12th {{Workshop}} on {{Argument Mining}}},
  author = {Bartz, Kilian and Lenz, Mirko and Bergmann, Ralph},
  editor = {Chistova, Elena and Cimiano, Philipp and Haddadan, Shohreh and Lapesa, Gabriella and {Ruiz-Dolz}, Ramon},
  year = {2025},
  month = jul,
  pages = {32--45},
  publisher = {Association for Computational Linguistics},
  address = {Vienna, Austria},
  doi = {10.18653/v1/2025.argmining-1.4},
  abstract = {Through manual annotation or automated argument mining processes, arguments can be represented not only as text, but also in structured formats like graphs. When searching for relevant arguments, this additional information about the relationship between their elementary units allows for the formulation of fine-grained structural constraints by using graphs as queries. Then, a retrieval can be performed by computing the similarity between the query and all available arguments. Previous works employed Graph Edit Distance (GED) algorithms such as A* search to compute mappings between nodes and edges for determining the similarity, which is rather expensive. In this paper, we propose an alternative based on Vision Transformers where arguments are rendered as images to obtain dense embeddings. We propose multiple space-filling visualizations and evaluate the retrieval performance of the vision-based approach against an existing A* search-based method. We find that our technique runs orders of magnitude faster than A* search and scales well on larger argument graphs while achieving competitive results.},
  isbn = {979-8-89176-258-9},
  url = {https://www.wi2.uni-trier.de/shared/publications/Bartz2025RetrievingArgumentGraphs.pdf}
}

Through manual annotation or automated argument mining processes, arguments can be represented not only as text, but also in structured formats like graphs. When searching for relevant arguments, this additional information about the relationship between their elementary units allows for the formulation of fine-grained structural constraints by using graphs as queries. Then, a retrieval can be performed by computing the similarity between the query and all available arguments. Previous works employed Graph Edit Distance (GED) algorithms such as A* search to compute mappings between nodes and edges for determining the similarity, which is rather expensive. In this paper, we propose an alternative based on Vision Transformers where arguments are rendered as images to obtain dense embeddings. We propose multiple space-filling visualizations and evaluate the retrieval performance of the vision-based approach against an existing A* search-based method. We find that our technique runs orders of magnitude faster than A* search and scales well on larger argument graphs while achieving competitive results.

@misc{clavie2025readbenchmeasuringdensetext,
  title={ReadBench: Measuring the Dense Text Visual Reading Ability of Vision-Language Models},
  author={Benjamin Clavié and Florian Brand},
  year={2025},
  eprint={2505.19091},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://www.wi2.uni-trier.de/shared/publications/clavie2025readbenchmeasuringdensetext.pdf},
}

@article{Warnecke2025FairnessSynthetic,
  title   = {Towards Fairness in Synthetic Healthcare Data: A Framework for the Evaluation of Synthetization Algorithms},
  author  = {Warnecke, Yannik and Kuhn, Martin and Diederichs, Felix and Brix, Tobias J and Clever, Lena and Bergmann, Ralph and Heider, Dominik and Storck, Michael},
  journal = {Studies in Health Technology and Informatics},
  volume  = {331},
  pages   = {25--34},
  year    = {2025},
  doi     = {10.3233/SHTI251376},
  issn    = {1879-8365},
  publisher = {IOS Press},
  address = {Netherlands},
  abstract = {Synthetic data generation is a rapidly evolving field, with significant potential for improving data privacy. However, evaluating the performance of synthetic data generation methods, especially the tradeoff between fairness and utility of the generated data, remains a challenge. In this work, we present our comprehensive framework, which evaluates fair synthetic data generation methods, benchmarking them against state-of-the-art synthesizers. The proposed framework consists of selection, evaluation, and application components that assess fairness, utility, and resemblance in real-world scenarios. The framework was applied to state-of-the-art data synthesizers, including TabFairGAN, DECAF, TVAE, and CTGAN, using a publicly available medical dataset. The results reveal the strengths and limitations of each synthesizer, including their bias mitigation strategies and trade-offs between fairness and utility, thereby showing the framework's effectiveness.},
  pmid    = {40899524},
  keywords = {Algorithms, Artificial Intelligence, Data Quality, Delivery of Health Care, Health Equity, Medical Informatics}
}

Synthetic data generation is a rapidly evolving field, with significant potential for improving data privacy. However, evaluating the performance of synthetic data generation methods, especially the tradeoff between fairness and utility of the generated data, remains a challenge. In this work, we present our comprehensive framework, which evaluates fair synthetic data generation methods, benchmarking them against state-of-the-art synthesizers. The proposed framework consists of selection, evaluation, and application components that assess fairness, utility, and resemblance in real-world scenarios. The framework was applied to state-of-the-art data synthesizers, including TabFairGAN, DECAF, TVAE, and CTGAN, using a publicly available medical dataset. The results reveal the strengths and limitations of each synthesizer, including their bias mitigation strategies and trade-offs between fairness and utility, thereby showing the framework's effectiveness.

@article{FORNARI2025101477,
title = {{Digital Twins of Business Processes: A Research Manifesto}},
journal = {Internet of Things},
volume = {30},
pages = {101477},
year = {2025},
issn = {2542-6605},
doi = {https://doi.org/10.1016/j.iot.2024.101477},
url={https://www.wi2.uni-trier.de/shared/publications/Fornari2025DigitalTwinsOfBP.pdf},
author = {Fabrizio Fornari and Ivan Compagnucci and Massimo {Callisto De Donato} and Yannis Bertrand and Harry H. Beyel and Emilio Carrión and Marco Franceschetti and Wolfgang Groher and Joscha Grüger and Emre Kilic and Agnes Koschmider and Francesco Leotta and Chiao-Yun Li and Giovani Lugaresi and Lukas Malburg and Juergen Mangler and Massimo Mecella and Oscar Pastor and Uwe Riss and Ronny Seiger and Estefania Serral and Victoria Torres and Pedro Valderas},
keywords = {Digital Twin, Business process, Internet of Things},
abstract = {Modern organizations necessitate continuous business processes improvement to maintain efficiency, adaptability, and competitiveness. In the last few years, the Internet of Things, via the deployment of sensors and actuators, has heavily been adopted in organizational and industrial settings to monitor and automatize physical processes influencing and enhancing how people and organizations work. Such advancements are now pushed forward by the rise of the Digital Twin paradigm applied to organizational processes. Advanced ways of managing and maintaining business processes come within reach as there is a Digital Twin of a business process - a virtual replica with real-time capabilities of a real process occurring in an organization. Combining business process models with real-time data and simulation capabilities promises to provide a new way to guide day-to-day organization activities. However, integrating Digital Twins and business processes is a non-trivial task, presenting numerous challenges and ambiguities. This manifesto paper aims to contribute to the current state of the art by clarifying the relationship between business processes and Digital Twins, identifying ongoing research and open challenges, thereby shedding light on and driving future exploration of this innovative interplay.}
}

Modern organizations necessitate continuous business processes improvement to maintain efficiency, adaptability, and competitiveness. In the last few years, the Internet of Things, via the deployment of sensors and actuators, has heavily been adopted in organizational and industrial settings to monitor and automatize physical processes influencing and enhancing how people and organizations work. Such advancements are now pushed forward by the rise of the Digital Twin paradigm applied to organizational processes. Advanced ways of managing and maintaining business processes come within reach as there is a Digital Twin of a business process - a virtual replica with real-time capabilities of a real process occurring in an organization. Combining business process models with real-time data and simulation capabilities promises to provide a new way to guide day-to-day organization activities. However, integrating Digital Twins and business processes is a non-trivial task, presenting numerous challenges and ambiguities. This manifesto paper aims to contribute to the current state of the art by clarifying the relationship between business processes and Digital Twins, identifying ongoing research and open challenges, thereby shedding light on and driving future exploration of this innovative interplay.

@article{Gruger2025Jun,
	author = {Grüger, Joscha and De Roock, Emmelien and Martin, Niels and Van Droogenbroeck, Filip and Bergmann, Ralph},
	title = {{Unveiling real-world process-related problems in hospitals: a Delphi study}},
	journal = {Process Sci.},
	volume = {2},
	number = {1},
	pages = {12--29},
	year = {2025},
	month = jun,
	issn = {2948-2178},
	publisher = {Springer International Publishing},
	doi = {10.1007/s44311-025-00016-6}
}

@inproceedings{kuhn2025probabilistic_beyond,
  author    = {Martin Kuhn and Joscha Grüger and Christoph Matheja and Andrey Rivkin},
  title     = {Probabilistic Programming for Trace Generation (and Beyond)},
  booktitle = {International Workshop on Algorithms \& Theories for the Analysis of Event Data (ATAED’25)},
  year      = {2025},
  address   = {Paris, France},
  month     = {June},
  publisher = {CEUR-WS.org},
  note      = {Workshop held as part of a satellite event 46th International Conference on Application and Theory of Petri Nets and Concurrency (Petri Nets 2025)},
  url       = {https://ceur-ws.org/Vol-3998/paper10.pdf}
}

@InProceedings{healthcare_decision_making_2025,
author="Gr{\"u}ger, Joscha
and Kuhn, Martin
and Amri, Karim
and Bergmann, Ralph",
editor="Delgado, Andrea
and Slaats, Tijs",
title="Research Paper: Enhancing Healthcare Decision-Making with Analogy-Based Reasoning",
booktitle="Process Mining Workshops",
year="2025",
publisher="Springer Nature Switzerland",
address="Cham",
pages="447--459",
abstract="Analogy-based reasoning is often employed in the treatment of hospitalized patients, especially when clinical guidelines or robust evidence bases are unavailable. This approach is based on the assumption that similar patients respond similarly to comparable treatments. Traditionally, this reasoning has relied on the memory and experience of physicians. However, the complexity of managing patient data---such as treatment sequences and responses---presents significant challenges without technological support. In particular, the procedural perspective of comparing patients is especially demanding. To address these challenges, we introduce the MAPI framework, an innovative approach for analogy-based, process-oriented search within patient data. This framework systematically manages treatment data, defines precise similarity measures, and retrieves comparable patient cases using case-based reasoning (CBR). By integrating analogy-based reasoning, MAPI enhances decision-making and improves the explainability of treatment choices, offering a more reliable and transparent tool for clinical practice.",
isbn="978-3-031-82225-4"
}

Analogy-based reasoning is often employed in the treatment of hospitalized patients, especially when clinical guidelines or robust evidence bases are unavailable. This approach is based on the assumption that similar patients respond similarly to comparable treatments. Traditionally, this reasoning has relied on the memory and experience of physicians. However, the complexity of managing patient data—such as treatment sequences and responses—presents significant challenges without technological support. In particular, the procedural perspective of comparing patients is especially demanding. To address these challenges, we introduce the MAPI framework, an innovative approach for analogy-based, process-oriented search within patient data. This framework systematically manages treatment data, defines precise similarity measures, and retrieves comparable patient cases using case-based reasoning (CBR). By integrating analogy-based reasoning, MAPI enhances decision-making and improves the explainability of treatment choices, offering a more reliable and transparent tool for clinical practice.

@InProceedings{CBR_CDSS_2025,
author="Kuhn, Martin
and Warnecke, Yannik
and Preciado-Marquez, Daniel
and Gr{\"u}ger, Joscha
and Bley, Laura Isabell
and Storck, Michael
and Weishaupt, Carsten
and Bergmann, Ralph
and Braun, Stephan Alexander",
editor="Bichindaritz, Isabelle
and L{\'o}pez, Beatriz",
title="Clinical Decision Support for Skin Tumor Treatment: A Case-Based Reasoning Approach",
booktitle="Case-Based Reasoning Research and Development",
year="2025",
publisher="Springer Nature Switzerland",
address="Cham",
pages="407--422",
abstract="Cancer treatment planning is a complex and individualized process due to the variability of patient-specific factors, tumor characteristics, and evolving medical standards. Predicting the next step in diagnosis or therapy remains a significant challenge, due to the high variability and limited availability of structured medical data. Clinical Decision Support Systems (CDSS) offer a promising solution, with Case-Based Reasoning (CBR) standing out for its ability to provide interpretable and transparent recommendations. Unlike black-box machine learning models, CBR leverages past cases to generate predictions by analogy, aligning with the way clinicians naturally reason. In this work, we propose a CBR-based CDSS for skin cancer treatment that integrates medical taxonomies and patient-specific clinical features to predict the next treatment step. By focusing on both technical performance and real-world application in a medical setting, this study provides insights for the deployment of CBR-based systems in medical practice.",
isbn="978-3-031-96559-3"
}

Cancer treatment planning is a complex and individualized process due to the variability of patient-specific factors, tumor characteristics, and evolving medical standards. Predicting the next step in diagnosis or therapy remains a significant challenge, due to the high variability and limited availability of structured medical data. Clinical Decision Support Systems (CDSS) offer a promising solution, with Case-Based Reasoning (CBR) standing out for its ability to provide interpretable and transparent recommendations. Unlike black-box machine learning models, CBR leverages past cases to generate predictions by analogy, aligning with the way clinicians naturally reason. In this work, we propose a CBR-based CDSS for skin cancer treatment that integrates medical taxonomies and patient-specific clinical features to predict the next treatment step. By focusing on both technical performance and real-world application in a medical setting, this study provides insights for the deployment of CBR-based systems in medical practice.

@misc{bewertung_edge_cloud_2025,
  title        = {{Bewertung von Edge-Cloud-Systemen: Vorgehen, Instrumente und Metriken}},
  author       = {Alt, Benjamin and Bast, Sebastian and Babel, Matthias and Buitmann, Julian and Creutz, Lars and Dartmann, Guido and Ehaus, Marvin and Engel, Thomas and Gast, Fabian and Großegesse, Nina and Guldner, Achim and Jahnke, Nils and Jilg, David and Körner, Marc-Fabian and Leskow, Justus and Mundorf, Johannes and Naumann, Stefan and Pehlken, Alexandra and Schacht, Marvin and Schick, Leo and Schlagenhauf, Tobias and Schultheis, Alexander and Schörner, Karsten and Straub, Sebastian and Strüker, Jens and Traphöner, Ralph and Weiher, Moritz-André},
  editor       = {Gabriel, Peter and Wittenbrink, Nicole},
  institution  = {Begleitforschung Edge Datenwirtschaft, im Auftrag des Bundesministeriums für Forschung, Technologie und Raumfahrt (BMFTR)},
  publisher    = {Institut für Innovation und Technik (iit) in der VDI/VDE Innovation + Technik GmbH},
  address      = {Berlin},
  year         = {2025},
  note         = {Erstellt im Rahmen der Begleitforschung zum Technologieprogramm "Edge Datenwirtschaft"},
  url          = {https://www.digitale-technologien.de/DT/Redaktion/DE/Downloads/Publikation/EDGE-Datenwirtschaft/2025_1107_EdgeCloudSystem.pdf},
  abstract 	   = {Edge-Cloud-Systeme bieten Unternehmen die Chance, innovative datenbasierte Dienste effizient und nachhaltig umzusetzen. Die Orientierungshilfe „Bewertung von Edge-Cloud-Systemen“ (ECS) bietet ein praxisnahes Vorgehensmodell mit Handlungsempfehlungen. Sie beschreibt, wie Edge- und Cloud-Komponenten sinnvoll kombiniert werden können, um Anforderungen wie geringe Latenz, Datenschutz und ökologische Nachhaltigkeit zu erfüllen. Im Mittelpunkt stehen konkrete Bewertungskriterien, Metriken und Instrumente, die helfen, ECS im jeweiligen Anwendungskontext systematisch zu analysieren. Beispiele aus realen Projekten zeigen, wie diese Kriterien operationalisiert werden können.}
}

Edge-Cloud-Systeme bieten Unternehmen die Chance, innovative datenbasierte Dienste effizient und nachhaltig umzusetzen. Die Orientierungshilfe „Bewertung von Edge-Cloud-Systemen“ (ECS) bietet ein praxisnahes Vorgehensmodell mit Handlungsempfehlungen. Sie beschreibt, wie Edge- und Cloud-Komponenten sinnvoll kombiniert werden können, um Anforderungen wie geringe Latenz, Datenschutz und ökologische Nachhaltigkeit zu erfüllen. Im Mittelpunkt stehen konkrete Bewertungskriterien, Metriken und Instrumente, die helfen, ECS im jeweiligen Anwendungskontext systematisch zu analysieren. Beispiele aus realen Projekten zeigen, wie diese Kriterien operationalisiert werden können.

@inproceedings{Bergmann2025EXARUnifiedExperienceGrounded,
  title = {{{EXAR}}: {{A Unified Experience-Grounded Agentic Reasoning Architecture}}},
  shorttitle = {{{EXAR}}},
  booktitle = {Case-{{Based Reasoning Research}} and {{Development}}},
  author = {Bergmann, Ralph and Brand, Florian and Lenz, Mirko and Malburg, Lukas},
  editor = {Bichindaritz, Isabelle and L{\'o}pez, Beatriz},
  year = {2025},
  series = {Lecture {{Notes}} in {{Computer Science}}},
  volume = {15662},
  pages = {3--17},
  publisher = {Springer Nature Switzerland},
  address = {Cham},
  doi = {10.1007/978-3-031-96559-3_1},
  abstract = {Current AI reasoning often relies on static pipelines (like the 4R cycle from Case-Based Reasoning (CBR) or standard Retrieval-Augmented Generation (RAG)) that limit adaptability. We argue it is time for a shift towards dynamic, experience-grounded agentic reasoning. This paper proposes EXAR, a new unified, experience-grounded architecture, conceptualizing reasoning not as a fixed sequence, but as a collaborative process orchestrated among specialized agents. EXAR integrates data and knowledge sources into a persistent Long-Term Memory utilized by diverse reasoning agents, which coordinate themselves via a Short-Term Memory. Governed by an Orchestrator and Meta Learner, EXAR enables flexible, context-aware reasoning strategies that adapt and improve over time, offering a blueprint for next-generation AI.},
  isbn = {978-3-031-96559-3},
  langid = {english},
  url = {https://www.wi2.uni-trier.de/shared/publications/Bergmann2025EXARUnifiedExperienceGrounded.pdf}
}

Current AI reasoning often relies on static pipelines (like the 4R cycle from Case-Based Reasoning (CBR) or standard Retrieval-Augmented Generation (RAG)) that limit adaptability. We argue it is time for a shift towards dynamic, experience-grounded agentic reasoning. This paper proposes EXAR, a new unified, experience-grounded architecture, conceptualizing reasoning not as a fixed sequence, but as a collaborative process orchestrated among specialized agents. EXAR integrates data and knowledge sources into a persistent Long-Term Memory utilized by diverse reasoning agents, which coordinate themselves via a Short-Term Memory. Governed by an Orchestrator and Meta Learner, EXAR enables flexible, context-aware reasoning strategies that adapt and improve over time, offering a blueprint for next-generation AI.

@inproceedings{Jimenez-Diaz2025FrameworkSupportingIterative,
  title = {A {{Framework}} for~{{Supporting}} the~{{Iterative Design}} of~{{CBR Applications}}},
  booktitle = {Case-{{Based Reasoning Research}} and {{Development}}},
  author = {{Jimenez-Diaz}, Guillermo and Lenz, Mirko and Malburg, Lukas and {D{\'i}az-Agudo}, Bel{\'e}n and Bergmann, Ralph},
  editor = {Bichindaritz, Isabelle and L{\'o}pez, Beatriz},
  year = {2025},
  series = {Lecture {{Notes}} in {{Computer Science}}},
  volume = {15662},
  pages = {252--266},
  publisher = {Springer Nature Switzerland},
  address = {Cham},
  doi = {10.1007/978-3-031-96559-3_17},
  abstract = {Iterative design is a well-known methodology that involves prototyping, testing, analyzing, and refining products or processes. Rapid prototyping and evaluation are crucial, enabling designers to quickly identify and resolve issues and iteratively improve the design. However, effective iterative design relies on tools that accelerate both prototype creation and visual evaluation. This paper aims to address this challenge by presenting a framework specifically designed to enhance the iterative design process for CBR applications. In this context, we emphasize the manual and knowledge-intensive task of defining similarity measures. Furthermore, we introduce a proof-of-concept implementation of this framework based on the CBRkit toolkit and the SimViz visualization tool. We studied the capabilities to support the iterative design of CBR applications through a case study in the prototypical cars domain.},
  isbn = {978-3-031-96559-3},
  langid = {english},
  url = {https://www.wi2.uni-trier.de/shared/publications/Jimenez-Diaz2025FrameworkSupportingIterative.pdf}
}

Iterative design is a well-known methodology that involves prototyping, testing, analyzing, and refining products or processes. Rapid prototyping and evaluation are crucial, enabling designers to quickly identify and resolve issues and iteratively improve the design. However, effective iterative design relies on tools that accelerate both prototype creation and visual evaluation. This paper aims to address this challenge by presenting a framework specifically designed to enhance the iterative design process for CBR applications. In this context, we emphasize the manual and knowledge-intensive task of defining similarity measures. Furthermore, we introduce a proof-of-concept implementation of this framework based on the CBRkit toolkit and the SimViz visualization tool. We studied the capabilities to support the iterative design of CBR applications through a case study in the prototypical cars domain.

@inproceedings{Lenz2025LLsiMLargeLanguage,
  title = {{{LLsiM}}: {{Large Language Models}} for~{{Similarity Assessment}} in~{{Case-Based Reasoning}}},
  shorttitle = {{{LLsiM}}},
  booktitle = {Case-{{Based Reasoning Research}} and {{Development}}},
  author = {Lenz, Mirko and Hoffmann, Maximilian and Bergmann, Ralph},
  editor = {Bichindaritz, Isabelle and L{\'o}pez, Beatriz},
  year = {2025},
  series = {Lecture {{Notes}} in {{Computer Science}}},
  volume = {15662},
  pages = {126--141},
  publisher = {Springer Nature Switzerland},
  address = {Cham},
  doi = {10.1007/978-3-031-96559-3_9},
  abstract = {In Case-Based Reasoning (CBR), past experience is used to solve new problems. Determining the most relevant cases is a crucial aspect of this process and is typically based on one or multiple manually-defined similarity measures, requiring deep domain knowledge. To overcome the knowledge-acquisition bottleneck, we propose the use of Large Language Models (LLMs) to automatically assess similarities between cases. We present three distinct approaches where the model is used for different tasks: (i) to predict similarity scores, (ii) to assess pairwise preferences, and (iii) to automatically configure similarity measures. Our conceptual work is accompanied by an open-source Python implementation that we use to evaluate the approaches on three different domains by comparing them to manually crafted similarity measures. Our results show that directly using LLM-based scores does not align well with the baseline rankings, but letting the LLM automatically configure the measures yields rankings that closely resemble the expert-defined ones.},
  isbn = {978-3-031-96559-3},
  langid = {english},
  url = {https://www.wi2.uni-trier.de/shared/publications/Lenz2025LLsiMLargeLanguage.pdf}
}

In Case-Based Reasoning (CBR), past experience is used to solve new problems. Determining the most relevant cases is a crucial aspect of this process and is typically based on one or multiple manually-defined similarity measures, requiring deep domain knowledge. To overcome the knowledge-acquisition bottleneck, we propose the use of Large Language Models (LLMs) to automatically assess similarities between cases. We present three distinct approaches where the model is used for different tasks: (i) to predict similarity scores, (ii) to assess pairwise preferences, and (iii) to automatically configure similarity measures. Our conceptual work is accompanied by an open-source Python implementation that we use to evaluate the approaches on three different domains by comparing them to manually crafted similarity measures. Our results show that directly using LLM-based scores does not align well with the baseline rankings, but letting the LLM automatically configure the measures yields rankings that closely resemble the expert-defined ones.

@inproceedings{Hotz_AdvancedSearchForRules_2025,
    author       = {Maxim Hotz and Lukas Malburg and Ralph Bergmann},
    title        = {{Advanced Search Techniques for Determining Optimal Sequences of Adaptation Rules in Process-Oriented Case-Based Reasoning}},
    booktitle    = {Case-Based Reasoning Research and Development - 33rd International Conference, {ICCBR} 2025, Biarritz, France, June 30 - July 3, 2025, Proceedings},
	series       = {Lecture Notes in Computer Science},
	pages        = {236--251},
	volume       = {15662},
	editor = {Bichindaritz, Isabelle and Lopez, Beatriz},
    publisher    = {Springer.},
    doi          = {10.1007/978-3-031-96559-3\_16},
    year         = {2025},
	keywords 	 = {Process-Oriented Case-Based Reasoning, Adaptive Workflow Management, Rule-Based Adaptation, Constrained Optimization Planning, Genetic Algorithms},
	abstract 	 = {The application of adaptation knowledge still poses a major challenge in modern Case-Based Reasoning (CBR) systems. This is especially the case in the subdomain of Process-Oriented Case-Based Reasoning (POCBR), where cases represent procedural experimental knowledge. Current adaptation methods in this field make use of proprietary local search techniques to apply adaptation knowledge, which is inefficient and does not allow exploitation of advanced search and optimization techniques. Therefore, this work presents an approach to transform rule-based adaptation into a planning problem that is solvable with both Constrained Optimization Planning (COP) and Genetic Algorithms (GA). The results of an experimental evaluation indicate that this transformation is feasible, although it does not achieve significant improvements in terms of adaptation quality without fine-tuning the default search parameters. However, integration into state-of-the-art planning frameworks builds the basis for using a variety of additional features and updates, enhancing the modeling and configuration process.},
	url = {https://www.wi2.uni-trier.de/shared/publications/2025_ICCBR_AdvancedSearchForRules_HotzEtAl.pdf}
}

The application of adaptation knowledge still poses a major challenge in modern Case-Based Reasoning (CBR) systems. This is especially the case in the subdomain of Process-Oriented Case-Based Reasoning (POCBR), where cases represent procedural experimental knowledge. Current adaptation methods in this field make use of proprietary local search techniques to apply adaptation knowledge, which is inefficient and does not allow exploitation of advanced search and optimization techniques. Therefore, this work presents an approach to transform rule-based adaptation into a planning problem that is solvable with both Constrained Optimization Planning (COP) and Genetic Algorithms (GA). The results of an experimental evaluation indicate that this transformation is feasible, although it does not achieve significant improvements in terms of adaptation quality without fine-tuning the default search parameters. However, integration into state-of-the-art planning frameworks builds the basis for using a variety of additional features and updates, enhancing the modeling and configuration process.

@inproceedings{SeigerSB2025,
    author		= {Ronny Seiger and Alexander Schultheis and Ralph Bergmann},
    title       = {{Case-Based Activity Detection from Segmented Internet of Things Data}},
    booktitle   = {Case-Based Reasoning Research and Development - 33rd International Conference, {ICCBR} 2025, Biarritz, France, June 30 - July 3rd, 2025, Proceedings},
	  series      = {Lecture Notes in Computer Science},
    pages       = {438--453},
    publisher   = {Springer.},
    year        = {2025},
  	doi         = {10.1007/978-3-031-96559-3_29},
  	keywords 	  = {Temporal Case-Based Reasoning, Time Series Data, Activity Detection, Internet of Things},
  	abstract 	  = {The use of Internet of Things (IoT) technologies drives the automation of business processes. However, such environments often lack process awareness and corresponding systems to monitor process executions. Due to its too fine-grained nature and variations, the direct use of data from IoT devices for monitoring is problematic, requiring an event abstraction step to lift the data to the business process level. This work investigates the application of Temporal Case-Based Reasoning (TCBR) as a novel experience-based approach to detect process activity executions in IoT data. The proposed TCBR approach uses activity signatures - representations of process and IoT data for an activity prototype - as a case base to classify unknown IoT time series data from a smart factory. A data flow architecture is presented that supports analysts in selecting a suitable activity prototype and evaluating its quality for activity detection. The results enable both, the development of high-quality activity detection services and the identification of improvement opportunities in IoT monitoring systems. The approach is evaluated using data produced by a smart factory. The results indicate that the TCBR methods used are very suitable for detecting activities in this IoT use case.},
    url         = {https://www.wi2.uni-trier.de/shared/publications/2025_ICCBR_SeigerEtAL.pdf}
}

The use of Internet of Things (IoT) technologies drives the automation of business processes. However, such environments often lack process awareness and corresponding systems to monitor process executions. Due to its too fine-grained nature and variations, the direct use of data from IoT devices for monitoring is problematic, requiring an event abstraction step to lift the data to the business process level. This work investigates the application of Temporal Case-Based Reasoning (TCBR) as a novel experience-based approach to detect process activity executions in IoT data. The proposed TCBR approach uses activity signatures - representations of process and IoT data for an activity prototype - as a case base to classify unknown IoT time series data from a smart factory. A data flow architecture is presented that supports analysts in selecting a suitable activity prototype and evaluating its quality for activity detection. The results enable both, the development of high-quality activity detection services and the identification of improvement opportunities in IoT monitoring systems. The approach is evaluated using data produced by a smart factory. The results indicate that the TCBR methods used are very suitable for detecting activities in this IoT use case.

@inproceedings{WeichSHB2025,
    author       = {Justin Weich and Alexander Schultheis and Maximilian Hoffmann and Ralph Bergmann},
    title        = {{Integration of Time Series Embedding for Efficient Retrieval in Case-Based Reasoning}},
    booktitle    = {Case-Based Reasoning Research and Development - 33rd International Conference, {ICCBR} 2025, Biarritz, France, June 30 - July 3rd, 2025, Proceedings},
	  series       = {Lecture Notes in Computer Science},
    pages        = {328--344},
    publisher    = {Springer.},
    year         = {2025},
  	doi 		     = {10.1007/978-3-031-96559-3_22},
  	keywords 	   = {Temporal Case-Based Reasoning, Time Series Data, Time Series Embedding, Time Series Similarity Measure, Siamese Neural Networks},
  	abstract 	   = {The increasing volume of time series data in Industry 4.0 applications creates substantial challenges for real-time data analysis. Such analyses that are conducted in the research area of Temporal Case-Based Reasoning (TCBR) face performance problems due to complex similarity measures. One potential approach already proven in other domains for addressing these problems is the usage of embedding techniques for time series data, which map these data into a simplified vector representation. Therefore, this paper investigates the integration of time series embedding techniques in the context of Case-Based Reasoning (CBR) to improve retrieval efficiency. Therefore, requirements for the application of embedding techniques in CBR are derived. A systematic literature study identifies possible approaches that are analyzed based on the requirements, with the result that no approach is suitable for the application. Therefore, a novel embedding architecture is proposed, using a Siamese neural network approach that can be trained with similarity values. The architecture is prototypically implemented in the ProCAKE framework and evaluated in an Internet of Things use case from a smart factory. The results demonstrate that the embedding-based retrieval achieves classification performance comparable to traditional similarity measures while significantly reducing retrieval time.},
    url         = {https://www.wi2.uni-trier.de/shared/publications/2025_ICCBR_WeichEtAL.pdf}
}

The increasing volume of time series data in Industry 4.0 applications creates substantial challenges for real-time data analysis. Such analyses that are conducted in the research area of Temporal Case-Based Reasoning (TCBR) face performance problems due to complex similarity measures. One potential approach already proven in other domains for addressing these problems is the usage of embedding techniques for time series data, which map these data into a simplified vector representation. Therefore, this paper investigates the integration of time series embedding techniques in the context of Case-Based Reasoning (CBR) to improve retrieval efficiency. Therefore, requirements for the application of embedding techniques in CBR are derived. A systematic literature study identifies possible approaches that are analyzed based on the requirements, with the result that no approach is suitable for the application. Therefore, a novel embedding architecture is proposed, using a Siamese neural network approach that can be trained with similarity values. The architecture is prototypically implemented in the ProCAKE framework and evaluated in an Internet of Things use case from a smart factory. The results demonstrate that the embedding-based retrieval achieves classification performance comparable to traditional similarity measures while significantly reducing retrieval time.

@inproceedings{BertrandSMGSAB2025,
  author       = {Yannis Bertrand and Alexander Schultheis and Lukas Malburg and Joscha Grüger and Estefan{\'{\i}}a {Serral Asensio} and Ralph Bergmann},
  title        = {{Challenges in Data Quality Management for IoT-Enhanced Event Logs}},
  booktitle    = {Research Challenges in Information Science - 19th International Conference, {RCIS} 2025, Guimar{\~{a}}es, Seville, Spain, 20 - 23 May, 2025, Proceedings},
  series       = {Lecture Notes in Business Information Processing},
  publisher    = {Springer},
  year         = {2025},
  doi 		     = {10.1007/978-3-031-92474-3_2},
  pages		     = {20--36},
  isbn		     = {978-3-031-92474-3},
  keywords 	   = {Business Process Management, Internet of Things, Data Quality Management, Data Quality Issues},
  abstract 	   = {Modern organizations make frequent use of Internet of Things (IoT) devices, such as sensors and actuators, to monitor and support their so-called IoT-enhanced Business Processes (BPs). These IoT devices collect vast amounts of data which, when processed appropriately, can yield crucial insights into the working of the BPs. However, IoT data, such as sensor data, is notoriously of poor quality, e.g., suffering from noise or having some missing data points. These problems are referred to as Data Quality Issues (DQIs), which often interfere with the analysis of IoT data in an industrial context. In this paper, we present a list of challenges that have to be tackled to achieve Data Quality (DQ) management in IoT-enhanced event logs. These challenges are derived and refined by leveraging expert knowledge and experience in DQIs within a focus group interview. In addition, we provide directions for solutions to these challenges based on input from the focus group interview and the literature. Finally, we discuss the challenges and their impact on typical DQ management tasks. The insights provided can help guide future research to achieve better DQ in event logs of IoT-enhanced BPs.},
  url 		      = {https://www.wi2.uni-trier.de/shared/publications/2025_RCIS_BertrandEtAl.pdf}
}

Modern organizations make frequent use of Internet of Things (IoT) devices, such as sensors and actuators, to monitor and support their so-called IoT-enhanced Business Processes (BPs). These IoT devices collect vast amounts of data which, when processed appropriately, can yield crucial insights into the working of the BPs. However, IoT data, such as sensor data, is notoriously of poor quality, e.g., suffering from noise or having some missing data points. These problems are referred to as Data Quality Issues (DQIs), which often interfere with the analysis of IoT data in an industrial context. In this paper, we present a list of challenges that have to be tackled to achieve Data Quality (DQ) management in IoT-enhanced event logs. These challenges are derived and refined by leveraging expert knowledge and experience in DQIs within a focus group interview. In addition, we provide directions for solutions to these challenges based on input from the focus group interview and the literature. Finally, we discuss the challenges and their impact on typical DQ management tasks. The insights provided can help guide future research to achieve better DQ in event logs of IoT-enhanced BPs.

@article{Klein_InformedAnomalyDetection_2025,
	title = {{Combining informed data-driven anomaly detection with knowledge graphs for root cause analysis in predictive maintenance}},
	journal = {Engineering Applications of Artificial Intelligence},
	volume = {145},
	pages = {110152},
	year = {2025},
	issn = {0952-1976},
	doi = {10.1016/j.engappai.2025.110152},
	author = {Patrick Klein and Lukas Malburg and Ralph Bergmann},
	keywords = {Hybrid artificial intelligence, Knowledge-based diagnosis, Data-driven anomaly detection, Predictive maintenance, Knowledge graph, Semantic web technologies},
	abstract = {Industry 4.0 has facilitated the access to sensor and actuator data from manufacturing systems, leading to studies on data-driven anomaly detection, but limited attention has been paid to finding root causes and automating this process using formalized expert knowledge. This is crucial due to the scarcity of qualified engineers and the time-consuming nature of diagnosing issues in large production systems. To address this gap, we present a framework that combines data-driven anomaly detection with a knowledge graph that provides domain knowledge by leveraging typical explanations of such models (i.e., data streams potentially caused the detection) for further diagnosis. The framework’s usefulness to infer affected components or data set labels has been evaluated using two deep anomaly detection approaches. For knowledge-based diagnosis, three query strategies that utilize various knowledge graph relationships are implemented through three Artificial Intelligence (AI) techniques. The proposed anomaly detection approach, informed by integrating expert knowledge via the graph structure of the knowledge graph and node embeddings for encoding time series, outperforms baselines and a deep autoencoder in detecting anomalies and in identifying anomalous data streams. In subsequent diagnosis, it achieves the best performance on a complete knowledge graph in combination with a graph pattern matching query by identifying the label or affected component in 60% of detected anomalies by providing 4.1 labels or 2.3 components until the correct one is identified. In case of a corrupted one, Symbolic-Driven Neural Reasoning (SDNR) and Case-Based Reasoning (CBR) with knowledge graph embeddings demonstrate advantages by halving the number of incorrect labels and unaffected components.},
	url = {https://www.wi2.uni-trier.de/shared/publications/2025_EAAI_KleinEtAl.pdf}
}

Industry 4.0 has facilitated the access to sensor and actuator data from manufacturing systems, leading to studies on data-driven anomaly detection, but limited attention has been paid to finding root causes and automating this process using formalized expert knowledge. This is crucial due to the scarcity of qualified engineers and the time-consuming nature of diagnosing issues in large production systems. To address this gap, we present a framework that combines data-driven anomaly detection with a knowledge graph that provides domain knowledge by leveraging typical explanations of such models (i.e., data streams potentially caused the detection) for further diagnosis. The framework’s usefulness to infer affected components or data set labels has been evaluated using two deep anomaly detection approaches. For knowledge-based diagnosis, three query strategies that utilize various knowledge graph relationships are implemented through three Artificial Intelligence (AI) techniques. The proposed anomaly detection approach, informed by integrating expert knowledge via the graph structure of the knowledge graph and node embeddings for encoding time series, outperforms baselines and a deep autoencoder in detecting anomalies and in identifying anomalous data streams. In subsequent diagnosis, it achieves the best performance on a complete knowledge graph in combination with a graph pattern matching query by identifying the label or affected component in 60% of detected anomalies by providing 4.1 labels or 2.3 components until the correct one is identified. In case of a corrupted one, Symbolic-Driven Neural Reasoning (SDNR) and Case-Based Reasoning (CBR) with knowledge graph embeddings demonstrate advantages by halving the number of incorrect labels and unaffected components.

@article{Hoffmann_Process_Augmentation_2025,
author = {Maximilian Hoffmann and Lukas Malburg and Ralph Bergmann},
title = {{Augmentation of Semantic Processes for Deep Learning Applications}},
journal = {Applied Artificial Intelligence},
volume = {39},
number = {1},
pages = {2506788},
year = {2025},
publisher = {Taylor \& Francis},
doi = {10.1080/08839514.2025.2506788},
URL = {https://www.wi2.uni-trier.de/shared/publications/2025_AAI_HoffmannEtAl.pdf}
}

@inproceedings{Lenz2025ArgueMapperAssistantInteractive,
  title = {{{ArgueMapper Assistant}}: {{Interactive Argument Mining Using Generative Language Models}}},
  shorttitle = {{{ArgueMapper Assistant}}},
  booktitle = {Artificial {{Intelligence XLI}}},
  author = {Lenz, Mirko and Bergmann, Ralph},
  editor = {Bramer, Max and Stahl, Frederic},
  year = {2025},
  series = {Lecture {{Notes}} in {{Computer Science}}},
  volume = {15446},
  pages = {189--203},
  publisher = {Springer Nature Switzerland},
  address = {Cham},
  doi = {10.1007/978-3-031-77915-2_14},
  abstract = {Structured arguments are a valuable resource for analyzing and understanding complex topics. However, manual annotation is time-consuming and often not feasible for large datasets, and automated approaches are less accurate. To address this issue, we propose an interactive argument mining system that takes advantage of generative language models to support humans in the creation of argument graphs. We present the open source ArgueMapper Assistant featuring two prompting strategies and evaluate it on a real-world news dataset. The resulting corpus containing 88 argument graphs is publicly available as well. With generative models, the annotation time is reduced by about 20\% while the number of errors is slightly increased (mostly due to missing argumentative units and wrong relation types). A survey provides insights into the usefulness and reliability of the assistant features and shows that participants prefer to use the assistant in the future.},
  isbn = {978-3-031-77915-2},
  langid = {english},
  url = {https://www.wi2.uni-trier.de/shared/publications/Lenz2025ArgueMapperAssistantInteractive.pdf}
}

Structured arguments are a valuable resource for analyzing and understanding complex topics. However, manual annotation is time-consuming and often not feasible for large datasets, and automated approaches are less accurate. To address this issue, we propose an interactive argument mining system that takes advantage of generative language models to support humans in the creation of argument graphs. We present the open source ArgueMapper Assistant featuring two prompting strategies and evaluate it on a real-world news dataset. The resulting corpus containing 88 argument graphs is publicly available as well. With generative models, the annotation time is reduced by about 20% while the number of errors is slightly increased (mostly due to missing argumentative units and wrong relation types). A survey provides insights into the usefulness and reliability of the assistant features and shows that participants prefer to use the assistant in the future.

@phdthesis{Hoffmann25,
	title        = {{Hybrid AI for Process Management - Improving Similarity Assessment in Process-Oriented Case-Based Reasoning via Deep Learning}},
	author       = {Hoffmann, Maximilian},
	year         = 2025,
	url          = {https://ubt.opus.hbz-nrw.de/files/2518/Dissertation_Hoffmann.pdf},
	school       = {University of Trier, Germany},
	note = {Licensed under Creative Commons 4.0 BY-NC.}
}

@inproceedings{LogPPL_2024,
title = "LogPPL: A Tool for Probabilistic Process Mining",
abstract = "This paper introduces LogPPL, a novel tool designed to bridge the gap between Data Petri Nets (DPNs) and probabilistic programming, enabling the generation of event logs with statistical guarantees via probabilistic program executions. LogPPL implements the transformation of DPNs into probabilistic programs written in the WebPPL language, allowing to harness the power of simulation and inference engines supplied for the WebPPL environment. Our tool simplifies the configuration of the DPN simulation setup and allows for exporting both event logs in XES format as well as WebPPL files. LogPPL capabilities are demonstrated through various scenarios, showcasing its potential to enhance process mining tasks by offering rigorous statistical modeling and advanced simulation features. The tool{\textquoteright}s design, features, and performance are evaluated, highlighting its utility in both academic and industrial settings.",
keywords = "DPN Simulation, Data Petri Nets, Event Log Generation, Probabilistic Programming, Process Mining, Statistical Simulation, WebPPL",
author = "Martin Kuhn and Joscha Gr{\"u}ger and Christoph Matheja and Andrey Rivkin",
year = "2024",
language = "English",
volume = "3783",
series = "CEUR Workshop Proceedings",
publisher = "CEUR-WS",
booktitle = "Proceedings of the ICPM 2024 Tool Demonstration Track",
note = "6th International Conference on Process Mining, ICPM",
}

This paper introduces LogPPL, a novel tool designed to bridge the gap between Data Petri Nets (DPNs) and probabilistic programming, enabling the generation of event logs with statistical guarantees via probabilistic program executions. LogPPL implements the transformation of DPNs into probabilistic programs written in the WebPPL language, allowing to harness the power of simulation and inference engines supplied for the WebPPL environment. Our tool simplifies the configuration of the DPN simulation setup and allows for exporting both event logs in XES format as well as WebPPL files. LogPPL capabilities are demonstrated through various scenarios, showcasing its potential to enhance process mining tasks by offering rigorous statistical modeling and advanced simulation features. The tool\textquoterights design, features, and performance are evaluated, highlighting its utility in both academic and industrial settings.

@InProceedings{DPN_meet_PPL_2024,
author="Kuhn, Martin
and Gr{\"u}ger, Joscha
and Matheja, Christoph
and Rivkin, Andrey",
editor="Marrella, Andrea
and Resinas, Manuel
and Jans, Mieke
and Rosemann, Michael",
title="Data Petri Nets Meet Probabilistic Programming",
booktitle="Business Process Management",
year="2024",
publisher="Springer Nature Switzerland",
address="Cham",
pages="21--38",
abstract="Probabilistic programming (PP) is a programming paradigm that allows for writing statistical models like ordinary programs, performing simulations by running those programs, and analyzing and refining their statistical behavior using powerful inference engines. This paper takes a step towards leveraging PP for reasoning about data-aware processes. To this end, we present a systematic translation of Data Petri Nets (DPNs) into a model written in a PP language whose features are supported by most PP systems. We show that our translation is sound and provides statistical guarantees for simulating DPNs. Furthermore, we discuss how PP can be used for process mining tasks and report on a prototype implementation of our translation. We also discuss further analysis scenarios that could be easily approached based on the proposed translation and available PP tools.",
isbn="978-3-031-70396-6"
}

Probabilistic programming (PP) is a programming paradigm that allows for writing statistical models like ordinary programs, performing simulations by running those programs, and analyzing and refining their statistical behavior using powerful inference engines. This paper takes a step towards leveraging PP for reasoning about data-aware processes. To this end, we present a systematic translation of Data Petri Nets (DPNs) into a model written in a PP language whose features are supported by most PP systems. We show that our translation is sound and provides statistical guarantees for simulating DPNs. Furthermore, we discuss how PP can be used for process mining tasks and report on a prototype implementation of our translation. We also discuss further analysis scenarios that could be easily approached based on the proposed translation and available PP tools.

@article{Fornari2024DigitalTwinsOfProcesses,
 author = {Fornari, Fabrizio and Compagnucci, Ivan and {Callisto De Donato}, Massimo and Bertrand, Yannis and Beyel, Harry H. and Carrión, Emilio and Franceschetti, Marco and Groher, Wolfgang and Grüger, Joscha and Kilic, Emre and Koschmider, Agnes and Leotta, Francesco and Li, Chiao-Yun and Lugaresi, Giovani and Malburg, Lukas and Mangler, Juergen and Mecella, Massimo and Pastor, Oscar and Riss, Uwe and Seiger, Ronny and Serral, Estefania and Torres, Victoria and Valderas, Pedro},
 year = {2024},
 title = {{Digital Twins of Business Processes: A research manifesto}},
 pages = {101477},
 issn = {2542-6605},
 journal = {{Internet of Things}},
 keywords = {{Digital Twin, Business process, Internet of Things}},
 abstract = {{Modern organizations necessitate continuous business processes improvement to maintain efficiency, adaptability, and competitiveness. In the last few years, the Internet of Things, via the deployment of sensors and actuators, has heavily been adopted in organizational and industrial settings to monitor and automatize physical processes influencing and enhancing how people and organizations work. Such advancements are now pushed forward by the rise of the Digital Twin paradigm applied to organizational processes. Advanced ways of managing and maintaining business processes come within reach as there is a Digital Twin of a business process - a virtual replica with real-time capabilities of a real process occurring in an organization. Combining business process models with real-time data and simulation capabilities promises to provide a new way to guide day-to-day organization activities. However, integrating Digital Twins and business processes is a non-trivial task, presenting numerous challenges and ambiguities. This manifesto paper aims to contribute to the current state of the art by clarifying the relationship between business processes and Digital Twins, identifying ongoing research and open challenges, thereby shedding light on and driving future exploration of this innovative interplay.}},
 doi = {10.1016/j.iot.2024.101477},
 url = {https://www.wi2.uni-trier.de/shared/publications/2024_IoT_DigitalTwinsOfBP_FornariEtAl.pdf}
}

Modern organizations necessitate continuous business processes improvement to maintain efficiency, adaptability, and competitiveness. In the last few years, the Internet of Things, via the deployment of sensors and actuators, has heavily been adopted in organizational and industrial settings to monitor and automatize physical processes influencing and enhancing how people and organizations work. Such advancements are now pushed forward by the rise of the Digital Twin paradigm applied to organizational processes. Advanced ways of managing and maintaining business processes come within reach as there is a Digital Twin of a business process - a virtual replica with real-time capabilities of a real process occurring in an organization. Combining business process models with real-time data and simulation capabilities promises to provide a new way to guide day-to-day organization activities. However, integrating Digital Twins and business processes is a non-trivial task, presenting numerous challenges and ambiguities. This manifesto paper aims to contribute to the current state of the art by clarifying the relationship between business processes and Digital Twins, identifying ongoing research and open challenges, thereby shedding light on and driving future exploration of this innovative interplay.

@article{Schultheis.2024_Processes,
  author       = {Schultheis, Alexander and Jilg, David and Malburg, Lukas and Bergweiler, Simon and Bergmann, Ralph},
  title        = {{Towards Flexible Control of Production Processes: A Requirements Analysis for Adaptive Workflow Management and Evaluation of Suitable Process Modeling Languages}},
  journal      = {Processes},
  volume 	   = {12},
  year 		   = {2024},
  number 	   = {12},
  article-number = {2714},
  year         = {2024},
  keywords 	   = {{Industry 4.0; Business Process Management; Adaptive Workflow Management; Imperative Process Modeling Languages; Flexible Control Processes}},
  abstract 	   = {In the context of Industry 4.0, Artificial Intelligence (AI) methods are used to maximize the efficiency and flexibility of production processes. The adaptive management of such semantic processes can optimize energy and resource efficiency while providing high reliability, but it depends on the representation type of these models. This paper provides a literature review of current Process Modeling Languages (PMLs). Based on a suitable PML, the flexibility of production processes can be increased. Currently, a common understanding of this process flexibility in the context of adaptive workflow management is missing. Therefore, requirements derived from the business environment are presented for process flexibility. To enable the identification of suitable PLMs, requirements regarding this are also raised. Based on these, the PMLs identified in the literature review are evaluated. Thereby, based on a preselection, a detailed examination of the seven most promising languages is performed, including an example from a real smart factory. As a result, a recommendation is made for the use of BPMN, for which it is presented how it can be enriched with separate semantic information that is suitable for the use of AI planning and, thus, enables flexible control.},
  doi = {10.3390/pr12122714},
  url = {https://www.wi2.uni-trier.de/shared/publications/2024_Processes_SchultheisEtAl.pdf}
}

In the context of Industry 4.0, Artificial Intelligence (AI) methods are used to maximize the efficiency and flexibility of production processes. The adaptive management of such semantic processes can optimize energy and resource efficiency while providing high reliability, but it depends on the representation type of these models. This paper provides a literature review of current Process Modeling Languages (PMLs). Based on a suitable PML, the flexibility of production processes can be increased. Currently, a common understanding of this process flexibility in the context of adaptive workflow management is missing. Therefore, requirements derived from the business environment are presented for process flexibility. To enable the identification of suitable PLMs, requirements regarding this are also raised. Based on these, the PMLs identified in the literature review are evaluated. Thereby, based on a preselection, a detailed examination of the seven most promising languages is performed, including an example from a real smart factory. As a result, a recommendation is made for the use of BPMN, for which it is presented how it can be enriched with separate semantic information that is suitable for the use of AI planning and, thus, enables flexible control.

@inproceedings{Seiger.2024_MicroserviceSmartFactorySoftware,
  author       = {Seiger, Ronny and Malburg, Lukas},
  title        = {{Revision of a Smart Factory Software Architecture from Monolith to Microservices}},
  booktitle    = {{Enterprise Design, Operations, and Computing. {EDOC} 2024 Forum, Vienna, Austria, September 10-13, 2024}},
  series       = {Lecture Notes in Business Information Processing},
  pages = {203--220},
  volume = {537},
  doi = {10.1007/978-3-031-79059-1_13},
  editor = {Kaczmarek-Heß, Monika and Rosenthal, Kristina and Suchánek, Marek and {Da Silva}, Miguel Mira and Proper, Henderik A. and Schnellmann, Marianne},
  publisher    = {Springer.},
  isbn = {978-3-031-79058-4},
  year         = {2024},
  abstract 	   = {{Software architecture plays an important role in the development of modern, complex software systems as it influences a system’s quality attributes and ability to grow with future demand. Designing the software architecture of cyber-physical systems (CPS) becomes even more challenging due to their capability of directly influencing the physical world and thus introducing new non-functional requirements related to fault-tolerance, safety, and resource scarcity. Existing research focuses on systems engineering to achieve the vertical integration of CPS with an organization’s information systems and processes, but not on software architecture to horizontally extend existing systems with new CPS. In this report we describe the process of revising an existing monolithic software architecture for a smart factory towards a microservices-based architecture to meet these new requirements and prepare the factory to be extended with new CPS. For the revision of the existing architecture, we provide an analysis of its code base before and after changes, a description of the refactoring process, and discuss relevant new nonfunctional requirements and architecture options. We elaborate on the architectural decisions favoring microservices and analyze the new architecture regarding improved quality attributes to evaluate the system.}},
  keywords 	   = {{Cyber-physical Systems, Software Architecture, Internet of Things, Microservices, Industry 4.0}},
  url = {https://www.wi2.uni-trier.de/shared/publications/2024_EDOC_MicroserviceSmartFactorySoftware_SeigerMalburg.pdf}
}

Software architecture plays an important role in the development of modern, complex software systems as it influences a system’s quality attributes and ability to grow with future demand. Designing the software architecture of cyber-physical systems (CPS) becomes even more challenging due to their capability of directly influencing the physical world and thus introducing new non-functional requirements related to fault-tolerance, safety, and resource scarcity. Existing research focuses on systems engineering to achieve the vertical integration of CPS with an organization’s information systems and processes, but not on software architecture to horizontally extend existing systems with new CPS. In this report we describe the process of revising an existing monolithic software architecture for a smart factory towards a microservices-based architecture to meet these new requirements and prepare the factory to be extended with new CPS. For the revision of the existing architecture, we provide an analysis of its code base before and after changes, a description of the refactoring process, and discuss relevant new nonfunctional requirements and architecture options. We elaborate on the architectural decisions favoring microservices and analyze the new architecture regarding improved quality attributes to evaluate the system.

@article{SchultheisEASY2024,
  author       = {Alexander Schultheis and Benjamin Alt and Sebastian Bast and Achim Guldner and David Jilg and Darko Katic and Johannes Mundorf and Tobias Schlagenhauf and Sebastian Weber and Ralph Bergmann and Simon Bergweiler and Lars Creutz and Guido Dartmann and Lukas Malburg and Stefan Naumann and Mahdi Rezapour and Martin Ruskowski},
  title        = {{EASY: Energy-Efficient Analysis and Control Processes in the Dynamic Edge-Cloud Continuum for Industrial Manufacturing}},
  journal      = {K{\"{u}}nstliche Intelligenz},
  year         = {2024},
  keywords 	   = {{Edge-Cloud Continuum, Energy- and Resource-Efficiency, Analysis and Control Processes}},
  abstract 	   = {According to the guiding principles of Industry 4.0, edge computing enables the data-sovereign and near-real-time processing of data directly at the point of origin. Using these edge devices in manufacturing organization will drive the use of industrial analysis, control, and Artificial Intelligence (AI) applications close to production. The goal of the EASY project is to make the added value of edge computing available by providing an easily usable edge-cloud continuum with a runtime environment and services for the execution of AI-based analysis and control processes. Within this continuum, a dynamic, distributed, and optimized execution of services is automated across the entire spectrum from centralized cloud to decentralized edge instances to increase productivity and resource efficiency.},
  doi = {10.1007/s13218-024-00868-3},
  url = {https://www.wi2.uni-trier.de/shared/publications/2024_KI_EASY_SchultheisEtAl.pdf}
}

According to the guiding principles of Industry 4.0, edge computing enables the data-sovereign and near-real-time processing of data directly at the point of origin. Using these edge devices in manufacturing organization will drive the use of industrial analysis, control, and Artificial Intelligence (AI) applications close to production. The goal of the EASY project is to make the added value of edge computing available by providing an easily usable edge-cloud continuum with a runtime environment and services for the execution of AI-based analysis and control processes. Within this continuum, a dynamic, distributed, and optimized execution of services is automated across the entire spectrum from centralized cloud to decentralized edge instances to increase productivity and resource efficiency.

@inproceedings{Lenz2024ArgServicesMicroserviceBasedArchitecture,
  title = {{{ArgServices}}: {{A Microservice-Based Architecture}} for~{{Argumentation Machines}}},
  shorttitle = {{{ArgServices}}},
  booktitle = {Robust {{Argumentation Machines}}},
  author = {Lenz, Mirko and Dumani, Lorik and Schenkel, Ralf and Bergmann, Ralph},
  editor = {Cimiano, Philipp and Frank, Anette and Kohlhase, Michael and Stein, Benno},
  year = {2024},
  series = {Lecture {{Notes}} in {{Computer Science}}},
  volume = {14638},
  pages = {352--369},
  publisher = {Springer Nature Switzerland},
  address = {Cham},
  doi = {10.1007/978-3-031-63536-6_21},
  abstract = {Argumentation is ubiquitous, and the development of argumentation machines could greatly assist humans in managing and navigating argumentation. However, the development of such systems is hindered by the lack of common standards and suitable tools, leading to ad-hoc solutions with little reuse value. Towards a more unified approach, we present an extensible microservice-based architecture for argumentation machines. Being built on the established gRPC framework, it provides strongly typed interfaces for the following services: (i) Argument Mining, (ii) Case-Based Reasoning on Arguments, (iii) Argument Retrieval and Ranking, and (iv) Quality Assessment of Arguments. Our system is designed to be extensible, allowing for easy integration of new tasks. We demonstrate the feasibility of our architecture via a proof-of-concept implementation and provide additional supplementary resources, such as a REST API gateway. Our contributions are publicly available on GitHub under the permissive MIT license.},
  isbn = {978-3-031-63536-6},
  langid = {english},
  url = {https://www.wi2.uni-trier.de/shared/publications/Lenz2024ArgServicesMicroserviceBasedArchitecture.pdf}
}

Argumentation is ubiquitous, and the development of argumentation machines could greatly assist humans in managing and navigating argumentation. However, the development of such systems is hindered by the lack of common standards and suitable tools, leading to ad-hoc solutions with little reuse value. Towards a more unified approach, we present an extensible microservice-based architecture for argumentation machines. Being built on the established gRPC framework, it provides strongly typed interfaces for the following services: (i) Argument Mining, (ii) Case-Based Reasoning on Arguments, (iii) Argument Retrieval and Ranking, and (iv) Quality Assessment of Arguments. Our system is designed to be extensible, allowing for easy integration of new tasks. We demonstrate the feasibility of our architecture via a proof-of-concept implementation and provide additional supplementary resources, such as a REST API gateway. Our contributions are publicly available on GitHub under the permissive MIT license.

@inproceedings{Lenz2024PolArgUnsupervisedPolarity,
  title = {{{PolArg}}: {{Unsupervised Polarity Prediction}} of~{{Arguments}} in~{{Real-Time Online Conversations}}},
  shorttitle = {{{PolArg}}},
  booktitle = {Robust {{Argumentation Machines}}},
  author = {Lenz, Mirko and Bergmann, Ralph},
  editor = {Cimiano, Philipp and Frank, Anette and Kohlhase, Michael and Stein, Benno},
  year = {2024},
  series = {Lecture {{Notes}} in {{Computer Science}}},
  volume = {14638},
  pages = {108--126},
  publisher = {Springer Nature Switzerland},
  address = {Cham},
  doi = {10.1007/978-3-031-63536-6_7},
  abstract = {The increasing usage of social networks has led to a growing number of discussions on the Internet that are a valuable source of argumentation that occurs in real time. Such conversations are often made up of a large number of participants and are characterized by a fast pace. Platforms like X/Twitter and Hacker News (HN) allow users to respond to other users' posts, leading to a tree-like structure. Previous work focused on training supervised models on datasets obtained from debate portals like Kialo where authors provide polarity labels (i.e., support/attack) together with their posts. Such classifiers may yield suboptimal predictions for the noisier posts from X or HN, so we propose unsupervised prompting strategies for large language models instead. Our experimental evaluation found this approach to be more effective for X conversations than a model fine-tuned on Kialo debates, but less effective for HN posts (which are more technical and less argumentative). Finally, we provide an open-source application for converting discussions on these platforms into argument graphs.},
  isbn = {978-3-031-63536-6},
  langid = {english},
  url = {https://www.wi2.uni-trier.de/shared/publications/Lenz2024PolArgUnsupervisedPolarity.pdf}
}

The increasing usage of social networks has led to a growing number of discussions on the Internet that are a valuable source of argumentation that occurs in real time. Such conversations are often made up of a large number of participants and are characterized by a fast pace. Platforms like X/Twitter and Hacker News (HN) allow users to respond to other users' posts, leading to a tree-like structure. Previous work focused on training supervised models on datasets obtained from debate portals like Kialo where authors provide polarity labels (i.e., support/attack) together with their posts. Such classifiers may yield suboptimal predictions for the noisier posts from X or HN, so we propose unsupervised prompting strategies for large language models instead. Our experimental evaluation found this approach to be more effective for X conversations than a model fine-tuned on Kialo debates, but less effective for HN posts (which are more technical and less argumentative). Finally, we provide an open-source application for converting discussions on these platforms into argument graphs.

@proceedings{Malburg.2024_ICCBRWorkshopProceedings,
  editor       = {Lukas Malburg},
  title        = {{Proceedings of the Workshops at the 32nd International Conference
                  on Case-Based Reasoning {(ICCBR-WS} 2024) co-located with the 32nd
                  International Conference on Case-Based Reasoning {(ICCBR} 2024), M{\'{e}}rida,
                  Mexico, July 1, 2024}},
  series       = {{CEUR} Workshop Proceedings},
  volume       = {3708},
  publisher    = {CEUR-WS.org},
  year         = {2024},
  url          = {http://sunsite.informatik.rwth-aachen.de/ftp/pub/publications/CEUR-WS/Vol-3708.zip}
}

@inproceedings{Brand.2024_LLMKnowledgeEngineer,
  author       = {Brand, Florian and Malburg, Lukas and Bergmann, Ralph},
  title        = {{Large Language Models as Knowledge Engineers}},
  booktitle    = {Proceedings of the Workshops at the 32nd International Conference
                  on Case-Based Reasoning {(ICCBR-WS} 2024) co-located with the 32nd
                  International Conference on Case-Based Reasoning {(ICCBR} 2024), M{\'{e}}rida,
                  Mexico, July 1, 2024},
  series       = {{CEUR} Workshop Proceedings},
  editor       = {Lukas Malburg},
  volume       = {3708},
  pages        = {3--18},
  publisher    = {CEUR-WS.org.},
  year         = {2024},
  abstract 	   = {{Many Artificial Intelligence (AI) systems require human-engineered knowledge at their core to reason about new problems based on this knowledge, with Case-Based Reasoning (CBR) being no exception. However, the acquisition of this knowledge is a time-consuming and laborious task for the domain experts that provide the needed knowledge. We propose an approach to help in the creation of this knowledge by leveraging Large Language Models (LLMs) in conjunction with existing knowledge to create the vocabulary and case base for a complex real-world domain. We find that LLMs are capable of generating knowledge, with results improving by using natural language and instructions. Furthermore, permissively licensed models like CodeLlama and Mixtral perform similar or better than closed state-of-the-art models like GPT-3.5 Turbo and GPT-4 Turbo.}},
  keywords 	   = {{Case-Based Reasoning, Knowledge Engineering, Knowledge Acquisition Bottleneck, Large Language Models, Prompting}},
  url = {https://www.wi2.uni-trier.de/shared/publications/2024_ICCBR-WS_LLMInCBR_BrandEtAl.pdf}
}

Many Artificial Intelligence (AI) systems require human-engineered knowledge at their core to reason about new problems based on this knowledge, with Case-Based Reasoning (CBR) being no exception. However, the acquisition of this knowledge is a time-consuming and laborious task for the domain experts that provide the needed knowledge. We propose an approach to help in the creation of this knowledge by leveraging Large Language Models (LLMs) in conjunction with existing knowledge to create the vocabulary and case base for a complex real-world domain. We find that LLMs are capable of generating knowledge, with results improving by using natural language and instructions. Furthermore, permissively licensed models like CodeLlama and Mixtral perform similar or better than closed state-of-the-art models like GPT-3.5 Turbo and GPT-4 Turbo.

@inproceedings{Schultheis.2024_MissingSensorValues,
  author       = {Schultheis, Alexander and Malburg, Lukas and Grüger, Joscha and Weich, Justin and Bertrand, Yannis and Bergmann, Ralph and Serral Asensio, Estefanía},
  title        = {{Identifying Missing Sensor Values in IoT Time Series Data: A Weight-Based Extension of Similarity Measures for Smart Manufacturing}},
  booktitle    = {Case-Based Reasoning Research and Development - 32nd International Conference, {ICCBR} 2024, Merida, Mexico, July 1-4, 2024, Proceedings},
  series       = {Lecture Notes in Computer Science},
  pages        = {240--257},
  volume       = {14775},
  publisher    = {Springer.},
  year         = {2024},
  doi          = {10.1007/978-3-031-63646-2\_16},
  abstract 	   = {{Smart Manufacturing integrates methods of Artificial Intelligence and the Internet of Things into processes to enhance efficiency and flexibility. However, analysis of time series sensor data, crucial for process optimization, is susceptible to Data Quality Issues (DQIs) and can lead to operational problems. Traditional machine learning approaches struggle with limited error data availability in addressing DQIs. The knowledge-driven approach of Case-Based Reasoning targets this issue by reusing experiences regarding already identified DQIs. While some DQIs can be detected using conventional similarity measures, the common, frequently occurring DQI type of Missing Sensor Values pose challenges that cannot be solved using established measures. To address this, this paper proposes a weight-based extension of similarity measures for time series data. This extension aims at the identification and handling of missing sensor values in smart manufacturing processes. Furthermore, analog extensions of established time series measures are presented and possible areas of application outside the DQI domain are outlined.}},
  keywords 	   = {{Temporal Case-Based Reasoning, Time Series Data, Time Series Similarity Measures, Data Quality Issues}},
  url = {https://www.wi2.uni-trier.de/shared/publications/2024_ICCBR_SchultheisEtAl.pdf}
 }

Smart Manufacturing integrates methods of Artificial Intelligence and the Internet of Things into processes to enhance efficiency and flexibility. However, analysis of time series sensor data, crucial for process optimization, is susceptible to Data Quality Issues (DQIs) and can lead to operational problems. Traditional machine learning approaches struggle with limited error data availability in addressing DQIs. The knowledge-driven approach of Case-Based Reasoning targets this issue by reusing experiences regarding already identified DQIs. While some DQIs can be detected using conventional similarity measures, the common, frequently occurring DQI type of Missing Sensor Values pose challenges that cannot be solved using established measures. To address this, this paper proposes a weight-based extension of similarity measures for time series data. This extension aims at the identification and handling of missing sensor values in smart manufacturing processes. Furthermore, analog extensions of established time series measures are presented and possible areas of application outside the DQI domain are outlined.

@inproceedings{Lenz2024CBRkitIntuitiveCaseBased,
  title = {{{CBRkit}}: {{An Intuitive Case-Based Reasoning Toolkit}} for~{{Python}}},
  shorttitle = {{{CBRkit}}},
  booktitle = {Case-{{Based Reasoning Research}} and {{Development}}},
  author = {Lenz, Mirko and Malburg, Lukas and Bergmann, Ralph},
  editor = {{Recio-Garcia}, Juan A. and {Orozco-del-Castillo}, Mauricio G. and Bridge, Derek},
  year = {2024},
  series = {Lecture {{Notes}} in {{Computer Science}}},
  volume = {14775},
  pages = {289--304},
  publisher = {Springer Nature Switzerland},
  address = {Cham},
  doi = {10.1007/978-3-031-63646-2_19},
  abstract = {Developing Case-Based Reasoning (CBR) applications is a complex and demanding task that requires a lot of experience and a deep understanding of users. Additionally, current CBR frameworks are not as usable as Machine Learning (ML) frameworks that can be deployed with only a few lines of code. To address these problems and allow users to easily build hybrid Artificial Intelligence (AI) systems by combining CBR with techniques such as ML, we present the CBRkit library in this paper. CBRkit is a Python-based framework that provides generic and easily extensible functions to simplify the creation of CBR applications with advanced similarity measures and case representations. The framework is available from GitHub and PyPI under the permissive MIT license. An initial user study indicates that it is easily possible even for non-CBR experts and users who only have limited Python programming skills to develop their own customized CBR application.},
  isbn = {978-3-031-63646-2},
  langid = {english},
  note = {Best Student Paper Award at ICCBR 2024},
  url = {https://www.wi2.uni-trier.de/shared/publications/Lenz2024CBRkitIntuitiveCaseBased.pdf}
}

Developing Case-Based Reasoning (CBR) applications is a complex and demanding task that requires a lot of experience and a deep understanding of users. Additionally, current CBR frameworks are not as usable as Machine Learning (ML) frameworks that can be deployed with only a few lines of code. To address these problems and allow users to easily build hybrid Artificial Intelligence (AI) systems by combining CBR with techniques such as ML, we present the CBRkit library in this paper. CBRkit is a Python-based framework that provides generic and easily extensible functions to simplify the creation of CBR applications with advanced similarity measures and case representations. The framework is available from GitHub and PyPI under the permissive MIT license. An initial user study indicates that it is easily possible even for non-CBR experts and users who only have limited Python programming skills to develop their own customized CBR application.

@inproceedings{Schultheis.2024,
  author       = {Schultheis, Alexander},
  title        = {{Exploring a Hybrid Case-Based Reasoning Approach for Time Series Adaptation in Predictive Maintenance}},
  booktitle    = {Proceedings of the Workshops at the 32nd International Conference
                  on Case-Based Reasoning {(ICCBR-WS} 2024) co-located with the 32nd
				  International Conference on Case-Based Reasoning {(ICCBR} 2024), Merida,
                  Mexico, July 1, 2024},
  series       = {{CEUR} Workshop Proceedings},
  publisher    = {CEUR-WS.org.},
  editor       = {Lukas Malburg},
  pages        = {230--235},
  volume       = {3708},
  year         = {2024},
  abstract 	   = {{Predictive Maintenance (PredM) is a vital concept within Industry 4.0, focusing on proactive machine maintenance through analysis of sensor data to uphold quality standards and prevent downtime. PredM traditionally employs data analysis methods or Machine Learning (ML) algorithms for anomaly detection in time series data from sensors. Despite ample error-free data, the occurrence of errors is rare. Case-Based Reasoning (CBR) offers an adaptive artificial intelligence approach effective in domains with limited fault data. The sub-research area of Temporal Case-Based Reasoning (TCBR) explores the processing of time series data based on CBR methods. Integrating TCBR methods into PredM leverages human involvement, addressing data privacy concerns and facilitating knowledge transfer.
  While the retrieval in TCBR has already been investigated, the adaptation of the time series contained in the retrieval results has not yet been considered. On this basis, however, it is possible to determine the further course of the time series as an alternative to ML prediction approaches. For the PredM use case with rare fault data, it is important to determine the further course of the time series and how much time remains before a possible fault case occurs. This research summary therefore investigates a hybrid CBR approach that uses deep learning methods like transformers for adaptation. The aim is to predict the course of a time series as accurately as possible, which is evaluated for the PredM use case. Such a hybrid CBR model should also extend an explanatory component for the predicted time series.}},
  keywords 	   = {{Temporal Case-Based Reasoning, Internet of Things, Time Series Data, Hybrid Case-Based Reasoning, Explainable Case-Based Reasoning, Predictive Maintenance}},
    url = {https://www.wi2.uni-trier.de/shared/publications/2024_ICCBR_DC_Schultheis.pdf}
}

Predictive Maintenance (PredM) is a vital concept within Industry 4.0, focusing on proactive machine maintenance through analysis of sensor data to uphold quality standards and prevent downtime. PredM traditionally employs data analysis methods or Machine Learning (ML) algorithms for anomaly detection in time series data from sensors. Despite ample error-free data, the occurrence of errors is rare. Case-Based Reasoning (CBR) offers an adaptive artificial intelligence approach effective in domains with limited fault data. The sub-research area of Temporal Case-Based Reasoning (TCBR) explores the processing of time series data based on CBR methods. Integrating TCBR methods into PredM leverages human involvement, addressing data privacy concerns and facilitating knowledge transfer. While the retrieval in TCBR has already been investigated, the adaptation of the time series contained in the retrieval results has not yet been considered. On this basis, however, it is possible to determine the further course of the time series as an alternative to ML prediction approaches. For the PredM use case with rare fault data, it is important to determine the further course of the time series and how much time remains before a possible fault case occurs. This research summary therefore investigates a hybrid CBR approach that uses deep learning methods like transformers for adaptation. The aim is to predict the course of a time series as accurately as possible, which is evaluated for the PredM use case. Such a hybrid CBR model should also extend an explanatory component for the predicted time series.

@inproceedings{Malburg.2024_AdaptationRulesInPOCBR,
  author       = {Malburg, Lukas and Hotz, Maxim and Bergmann, Ralph},
  title        = {{Improving Complex Adaptations in Process-Oriented Case-Based Reasoning by Applying Rule-Based Adaptation}},
  booktitle    = {Case-Based Reasoning Research and Development - 32nd International Conference, {ICCBR} 2024, Merida, Mexico, July 1-4, 2024, Proceedings},
  series       = {Lecture Notes in Computer Science},
  pages        = {50--66},
  volume       = {14775},
  doi          = {10.1007/978-3-031-63646-2\_4},
  publisher    = {Springer.},
  year         = {2024},
  abstract 	   = {{Adaptation is a complex and error-prone task in Case-Based Reasoning (CBR), including the adaptation knowledge acquisition and modeling efforts required for performing adaptations. This is also evident for the subfield of Process-Oriented Case-Based Reasoning (POCBR) in which cases represent procedural experiential knowledge, making creation and maintaining adaptation knowledge even for domain experts exceedingly challenging. Current adaptation methods in POCBR address the adaptation knowledge bottleneck by learning adaptation knowledge based on cases in the case base. However, these approaches are based on proprietary representation formats, resulting in low usability and maintainability. Therefore, we present an approach of using adaptation rules and rule engines for complex adaptations in POCBR in this paper. The results of an experimental evaluation indicate that the rule-based adaptation approach leads to significantly better results during runtime than an already available POCBR adaptation method.}},
  keywords 	   = {{Process-Oriented Case-Based Reasoning, Adaptive Workflow Management, Rule-Based Adaptation, Drools Rule Engine, Adaptation Operators}},
  url = {https://www.wi2.uni-trier.de/shared/publications/2024_ICCBR_RuleBasedAdaptation_MalburgEtAl.pdf}
}

Adaptation is a complex and error-prone task in Case-Based Reasoning (CBR), including the adaptation knowledge acquisition and modeling efforts required for performing adaptations. This is also evident for the subfield of Process-Oriented Case-Based Reasoning (POCBR) in which cases represent procedural experiential knowledge, making creation and maintaining adaptation knowledge even for domain experts exceedingly challenging. Current adaptation methods in POCBR address the adaptation knowledge bottleneck by learning adaptation knowledge based on cases in the case base. However, these approaches are based on proprietary representation formats, resulting in low usability and maintainability. Therefore, we present an approach of using adaptation rules and rule engines for complex adaptations in POCBR in this paper. The results of an experimental evaluation indicate that the rule-based adaptation approach leads to significantly better results during runtime than an already available POCBR adaptation method.

@Article{Grumbach2024KI,
author={Grumbach, Lisa},
title={Dissertation Abstract: Flexible Workflows - A Constraint- and Case-Based Approach},
journal={KI - K{\"u}nstliche Intelligenz},
year={2024},
month={Aug},
day={12},
issn={1610-1987},
doi={10.1007/s13218-024-00869-2},
url={https://doi.org/10.1007/s13218-024-00869-2}
}

@article{2024_Sensors_MachhamerEtAl,
  author={Machhamer, Rüdiger and Begic Fazlic, Lejla and Guven, Eray and Junk, David and Karabulut Kurt, Gunes and Naumann, Stefan and Didas, Stephan and Gollmer, Klaus-Uwe and Bergmann, Ralph and Timm, Ingo J. and Dartmann, Guido},
  journal={IEEE Sensors Journal},
  title={{Likelihood-Based Sensor Calibration Using Affine Transformation}},
  year={2024},
  volume={24},
  number={3},
  pages={3672-3680},
  abstract = {An important task in the field of sensor technology is the efficient implementation of adaptation procedures of measurements from one sensor to another sensor of identical design. One idea is to use the estimation of an affine transformation (AT) between different systems, which can be improved by the knowledge of experts. This article presents an improved solution from Glacier Research that was published back in 1973. The results demonstrate the adaptability of this solution for various applications, including software calibration of sensors, implementation of expert-based adaptation, and paving the way for future advancements such as distributed learning methods. One idea here is to use the knowledge of experts for estimating an AT between different systems. We evaluate our research with simulations and also with real measured data of a multisensor board with eight identical sensors. Both dataset and evaluation script are provided for download. The results show an improvement for both the simulation and the experiments with real data.},
  keywords={Sensors;Maximum likelihood estimation;Estimation;Sensor systems;Data models;Adaptation models;Calibration;Keywords Distributed learning;expert supported learning;sensor adaptation;transformation of sensors},
  doi={10.1109/JSEN.2023.3341503}
}

An important task in the field of sensor technology is the efficient implementation of adaptation procedures of measurements from one sensor to another sensor of identical design. One idea is to use the estimation of an affine transformation (AT) between different systems, which can be improved by the knowledge of experts. This article presents an improved solution from Glacier Research that was published back in 1973. The results demonstrate the adaptability of this solution for various applications, including software calibration of sensors, implementation of expert-based adaptation, and paving the way for future advancements such as distributed learning methods. One idea here is to use the knowledge of experts for estimating an AT between different systems. We evaluate our research with simulations and also with real measured data of a multisensor board with eight identical sensors. Both dataset and evaluation script are provided for download. The results show an improvement for both the simulation and the experiments with real data.

@inproceedings{GrumbachWB24,
  author       = {Lisa Grumbach and
                  Alexander Winzig and
                  Ralph Bergmann},
  editor       = {Juan A. Recio{-}Garc{\'{\i}}a and
                  Mauricio Gabriel Orozco{-}del{-}Castillo and
                  Derek Bridge},
  title        = {Towards a Case-Based Support for Responding Emergency Calls},
  booktitle    = {Case-Based Reasoning Research and Development - 32nd International
                  Conference, {ICCBR} 2024, Merida, Mexico, July 1-4, 2024, Proceedings},
  series       = {Lecture Notes in Computer Science},
  volume       = {14775},
  pages        = {273--288},
  publisher    = {Springer},
  year         = {2024},
  url          = {http://www.wi2.uni-trier.de/shared/publications/2024_ICCBR_Grumbach.pdf},
  doi          = {10.1007/978-3-031-63646-2\_18},
  timestamp    = {Wed, 10 Jul 2024 20:46:39 +0200},
  biburl       = {https://dblp.org/rec/conf/iccbr/GrumbachWB24.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@InProceedings{weighted_violations_cc_2023,
author="Gr{\"u}ger, Joscha
and Geyer, Tobias
and Kuhn, Martin
and Braun, Stephan A.
and Bergmann, Ralph",
editor="Montali, Marco
and Senderovich, Arik
and Weidlich, Matthias",
title="Weighted Violations in Alignment-Based Conformance Checking",
booktitle="Process Mining Workshops",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="289--301",
abstract="Conformance checking is a process mining technique that allows verifying the conformance of process instances to a given model. Many conformance checking algorithms provide quantitative information about the conformance of a process instance through metrics such as fitness. Fitness measures to what degree the model allows the behavior observed in the event log. Conventional fitness does not consider the individual severity of deviations. In cases where there are rules that are more important to comply with than others, fitness consequently does not take all factors into account. In the field of medicine, for example, there are guideline recommendations for clinical treatment that have information about their importance and soundness, making it essential to distinguish between them. Therefore, we introduce an alignment-based conformance checking approach that considers the importance of individual specifications and weights violations. The approach is evaluated with real patient data and evidence-based guideline recommendations. Using this approach, it was possible to integrate guideline recommendation metadata into the conformance checking process and to weight violations individually.",
isbn="978-3-031-27815-0"
}

Conformance checking is a process mining technique that allows verifying the conformance of process instances to a given model. Many conformance checking algorithms provide quantitative information about the conformance of a process instance through metrics such as fitness. Fitness measures to what degree the model allows the behavior observed in the event log. Conventional fitness does not consider the individual severity of deviations. In cases where there are rules that are more important to comply with than others, fitness consequently does not take all factors into account. In the field of medicine, for example, there are guideline recommendations for clinical treatment that have information about their importance and soundness, making it essential to distinguish between them. Therefore, we introduce an alignment-based conformance checking approach that considers the importance of individual specifications and weights violations. The approach is evaluated with real patient data and evidence-based guideline recommendations. Using this approach, it was possible to integrate guideline recommendation metadata into the conformance checking process and to weight violations individually.

@InProceedings{declarative_cc_2023,
author="Gr{\"u}ger, Joscha
and Geyer, Tobias
and Kuhn, Martin
and Braun, Stephan A.
and Bergmann, Ralph",
editor="Cabanillas, Cristina
and Garmann-Johnsen, Niels Frederik
and Koschmider, Agnes",
title="Declarative Guideline Conformance Checking of Clinical Treatments: A Case Study",
booktitle="Business Process Management Workshops",
year="2023",
publisher="Springer International Publishing",
address="Cham",
pages="274--285",
abstract="Conformance checking is a process mining technique that allows verifying the conformance of process instances to a given model. Thus, this technique is predestined to be used in the medical context for the comparison of treatment cases with clinical guidelines. However, medical processes are highly variable, highly dynamic, and complex. This makes the use of imperative conformance checking approaches in the medical domain difficult. Studies show that declarative approaches can better address these characteristics. However, none of the approaches has yet gained practical acceptance. Another challenge are alignments, which usually do not add any value from a medical point of view. For this reason, we investigate in a case study the usability of the HL7 standard Arden Syntax for declarative, rule-based conformance checking and the use of manually modeled alignments. Using the approach, it was possible to check the conformance of treatment cases and create medically meaningful alignments for large parts of a medical guideline.",
isbn="978-3-031-25383-6"
}

Conformance checking is a process mining technique that allows verifying the conformance of process instances to a given model. Thus, this technique is predestined to be used in the medical context for the comparison of treatment cases with clinical guidelines. However, medical processes are highly variable, highly dynamic, and complex. This makes the use of imperative conformance checking approaches in the medical domain difficult. Studies show that declarative approaches can better address these characteristics. However, none of the approaches has yet gained practical acceptance. Another challenge are alignments, which usually do not add any value from a medical point of view. For this reason, we investigate in a case study the usability of the HL7 standard Arden Syntax for declarative, rule-based conformance checking and the use of manually modeled alignments. Using the approach, it was possible to check the conformance of treatment cases and create medically meaningful alignments for large parts of a medical guideline.

@inproceedings{Lenz2023CaseBasedAdaptationArgument,
  title = {Case-{{Based Adaptation}} of~{{Argument Graphs}} with~{{WordNet}} and~{{Large Language Models}}},
  booktitle = {Case-{{Based Reasoning Research}} and {{Development}}},
  author = {Lenz, Mirko and Bergmann, Ralph},
  editor = {Massie, Stewart and Chakraborti, Sutanu},
  year = {2023},
  series = {Lecture {{Notes}} in {{Computer Science}}},
  volume = {14141},
  pages = {263--278},
  publisher = {Springer Nature Switzerland},
  address = {Cham},
  doi = {10.1007/978-3-031-40177-0_17},
  abstract = {Finding information online is hard, even more so once you get into the domain of argumentation. There have been developments around the specialized argumentation machines that incorporate structural features of arguments, but all current approaches share one pitfall: They operate on a corpora of limited sizes. Consequently, it may happen that a user searches for a rather general term like cost increases, but the machine is only able to serve arguments concerned with rent increases. We aim to bridge this gap by introducing approaches to generalize/specialize a found argument using a combination of WordNet and Large Language Models. The techniques are evaluated on a new benchmark dataset with diverse queries using our fully featured implementation. Both the dataset and the code are publicly available on GitHub.},
  isbn = {978-3-031-40177-0},
  langid = {english},
  note = {Best Student Paper Award at ICCBR 2023},
  url = {http://www.wi2.uni-trier.de/shared/publications/Lenz2023CaseBasedAdaptationArgument.pdf}
}

Finding information online is hard, even more so once you get into the domain of argumentation. There have been developments around the specialized argumentation machines that incorporate structural features of arguments, but all current approaches share one pitfall: They operate on a corpora of limited sizes. Consequently, it may happen that a user searches for a rather general term like cost increases, but the machine is only able to serve arguments concerned with rent increases. We aim to bridge this gap by introducing approaches to generalize/specialize a found argument using a combination of WordNet and Large Language Models. The techniques are evaluated on a new benchmark dataset with diverse queries using our fully featured implementation. Both the dataset and the code are publicly available on GitHub.

@inproceedings{theusch2023towards,
  author	= {Theusch, Felix and Seemann, Lukas and Guldner, Achim and Naumann, Stefan and Bergmann, Ralph},
  title		= {Towards Machine Learning-based Digital Twins in Cyber-Physical Systems},
  editor       	= {Lombardo, Gianfranco and Picone, Marco and Recupero, Diego and Vizzari, Guiseppe},
  booktitle    	= {Proceedings of the 1st Workshop on AI for Digital Twins and Cyber-Physical Applications (AI4DT&CP 2023), in conjunction with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao S.A.R., August 19, 2023},
  year		= {2023},
  url = {https://ceur-ws.org/Vol-3541/1.pdf}
}

@InProceedings{Schuler.2023_SemiSupervisedTransferLearning,
author="Schuler, Nicolas and Hoffmann, Maximilian and Beise, Hans-Peter and Bergmann, Ralph",
editor="Bramer, Max and Stahl, Frederic",
title="Semi-supervised Similarity Learning in Process-Oriented Case-Based Reasoning",
booktitle="Artificial Intelligence XL",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="159--173",
abstract="Supervised learning is typically challenging with insufficient amounts of labeled training data and high costs for label acquisition, creating a demand for unsupervised learning methods. In the research area of Process-Oriented Case-Based Reasoning (POCBR), this demand is created by training data that is manually-modeled and computationally-expensive labeling methods. In this paper, we propose a semi-supervised transfer learning method for learning similarities between pairs of semantic graphs in POCBR with Graph Neural Networks (GNNs). The method aims to replace the fully supervised learning procedure from previous work with an unsupervised and a supervised training phase. In the first phase, the GNNs are pretrained with a triplet learning procedure that utilizes graph augmentation and random selection to enable unsupervised training. This phase is followed by a supervised one where the pretrained model is trained on the original labeled training data. The experimental evaluation examines the quality of the semi-supervised models compared to the supervised models from previous work for three semantic graph domains with different properties. The results indicate the potential of the proposed approach for improving retrieval quality.",
isbn="978-3-031-47994-6",
url="https://www.wi2.uni-trier.de/shared/publications/2023_SGAI_Schuler.pdf"
}

Supervised learning is typically challenging with insufficient amounts of labeled training data and high costs for label acquisition, creating a demand for unsupervised learning methods. In the research area of Process-Oriented Case-Based Reasoning (POCBR), this demand is created by training data that is manually-modeled and computationally-expensive labeling methods. In this paper, we propose a semi-supervised transfer learning method for learning similarities between pairs of semantic graphs in POCBR with Graph Neural Networks (GNNs). The method aims to replace the fully supervised learning procedure from previous work with an unsupervised and a supervised training phase. In the first phase, the GNNs are pretrained with a triplet learning procedure that utilizes graph augmentation and random selection to enable unsupervised training. This phase is followed by a supervised one where the pretrained model is trained on the original labeled training data. The experimental evaluation examines the quality of the semi-supervised models compared to the supervised models from previous work for three semantic graph domains with different properties. The results indicate the potential of the proposed approach for improving retrieval quality.

@inproceedings{JilgGGB2023,
  author       = {David Jilg and
                  Joscha Gr{\"{u}}ger and
                  Tobias Geyer and
                  Ralph Bergmann},
  editor       = {Dirk Fahland and
                  Andr{\'{e}}s Jim{\'{e}}nez{-}Ram{\'{\i}}rez and
                  Akhil Kumar and
                  Jan Mendling and
                  Brian T. Pentland and
                  Stefanie Rinderle{-}Ma and
                  Tijs Slaats and
                  Johan Versendaal and
                  Barbara Weber and
                  Mathias Weske and
                  Karolin Winter},
  title        = {{DALG:} The Data Aware Event Log Generator},
  booktitle    = {Proceedings of the Best Dissertation Award, Doctoral Consortium, and
                  Demonstration {\&} Resources Forum at {BPM} 2023 co-located with
                  21st International Conference on Business Process Management {(BPM}
                  2023), Utrecht, The Netherlands, September 11th to 15th, 2023},
  series       = {{CEUR} Workshop Proceedings},
  volume       = {3469},
  pages        = {142--146},
  publisher    = {CEUR-WS.org},
  year         = {2023},
  url          = {https://ceur-ws.org/Vol-3469/paper-26.pdf},
  timestamp    = {Wed, 06 Sep 2023 08:40:08 +0200},
  biburl       = {https://dblp.org/rec/conf/bpm/JilgGGB23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{MitraEtAl2023,
  author       = {Mitra, Shania and Mathew, Ditty and P, Deepak and Chakraborti, Sutanu},
  title        = {{Group fairness in case-based reasoning}},
  booktitle    = {Case-Based Reasoning Research and Development - 31st International Conference, {ICCBR} 2023, Aberdeen, Scotland, July 17-20, 2023, Proceedings},
  series       = {Lecture Notes in Computer Science},
  volume       = {14141},
  pages        = {217--232},
  publisher    = {Springer},
  year         = {2023},
  doi		   = {10.1007/978-3-031-40177-0_14},
  abstract 	   = {There has been a significant recent interest in algorithmic fairness within data-driven systems. In this paper, we consider group fairness within Case-based Reasoning. Group fairness targets to ensure parity of outcomes across pre-specified sensitive groups, defined on the basis of extant entrenched discrimination. Addressing the context of binary decision choice scenarios over binary sensitive attributes, we develop three separate fairness interventions that operate at different stages of the CBR process. These techniques, called Label Flipping (LF), Case Weighting (CW) and Weighted Adaptation (WA), use distinct strategies to enhance group fairness in CBR decision making. Through an extensive empirical evaluation over several popular datasets and against natural baseline methods, we show that our methods are able to achieve significant enhancements in fairness at low detriment to accuracy, thus illustrating effectiveness of our methods at advancing fairness.},
}

There has been a significant recent interest in algorithmic fairness within data-driven systems. In this paper, we consider group fairness within Case-based Reasoning. Group fairness targets to ensure parity of outcomes across pre-specified sensitive groups, defined on the basis of extant entrenched discrimination. Addressing the context of binary decision choice scenarios over binary sensitive attributes, we develop three separate fairness interventions that operate at different stages of the CBR process. These techniques, called Label Flipping (LF), Case Weighting (CW) and Weighted Adaptation (WA), use distinct strategies to enhance group fairness in CBR decision making. Through an extensive empirical evaluation over several popular datasets and against natural baseline methods, we show that our methods are able to achieve significant enhancements in fairness at low detriment to accuracy, thus illustrating effectiveness of our methods at advancing fairness.

@inproceedings{SchultheisZB2023,
  author       = {Schultheis, Alexander and Zeyen, Christian and Bergmann, Ralph},
  title        = {{An Overview and Comparison of Case-Based Reasoning Frameworks}},
  booktitle    = {Case-Based Reasoning Research and Development - 31st International Conference, {ICCBR} 2023, Aberdeen, Scotland, July 17-20, 2023, Proceedings},
  series       = {Lecture Notes in Computer Science},
  volume       = {14141},
  pages        = {327--343},
  publisher    = {Springer},
  year         = {2023},
  doi		   = {10.1007/978-3-031-40177-0_21},
  url		   = {https://www.wi2.uni-trier.de/shared/publications/2023_ICCBR_SchultheisZB.pdf},
  abstract 	   = {Case-Based Reasoning (CBR) is a methodology with many applications in industrial and scientific domains. Over the past decades, various frameworks have been developed to facilitate the development of CBR applications. For practitioners and researchers, it is challenging to overview the landscape of existing frameworks with their specific scope and features. This makes it difficult to choose the most suitable framework for specific requirements. To address this issue, this work provides an overview and comparison of CBR frameworks, focusing on five recent, open-source CBR frameworks: CloodCBR, eXiT*CBR, jColibri, myCBR, and ProCAKE. They are compared by supported CBR types, knowledge containers, CBR phases, interfaces, and special features.},
  keywords 	   = {Case-Based Reasoning, CBR Framework, CBR Applications, CloodCBR, eXiT*CBR, jColibri, myCBR, ProCAKE}
}

Case-Based Reasoning (CBR) is a methodology with many applications in industrial and scientific domains. Over the past decades, various frameworks have been developed to facilitate the development of CBR applications. For practitioners and researchers, it is challenging to overview the landscape of existing frameworks with their specific scope and features. This makes it difficult to choose the most suitable framework for specific requirements. To address this issue, this work provides an overview and comparison of CBR frameworks, focusing on five recent, open-source CBR frameworks: CloodCBR, eXiT*CBR, jColibri, myCBR, and ProCAKE. They are compared by supported CBR types, knowledge containers, CBR phases, interfaces, and special features.

@inproceedings{SchultheisHMB2023,
  author       = {Schultheis, Alexander and Hoffmann, Maximilian and Malburg, Lukas and Bergmann, Ralph},
  title        = {{Explanation of Similarities in Process-Oriented Case-Based Reasoning by Visualization}},
  booktitle    = {Case-Based Reasoning Research and Development - 31st International Conference, {ICCBR} 2023, Aberdeen, Scotland, July 17-20, 2023, Proceedings},
  series       = {Lecture Notes in Computer Science},
  volume       = {14141},
  pages        = {53--68},
  publisher    = {Springer},
  year         = {2023},
  doi		   = {10.1007/978-3-031-40177-0_4},
  url		   = {https://www.wi2.uni-trier.de/shared/publications/2023_ICCBR_SchultheisHMB.pdf},
  abstract 	   = {Modeling similarity measures in Case-Based Reasoning is a knowledge-intensive, demanding, and error-prone task even for domain experts. Visualizations offer support for users, but are currently only available for certain subdomains and case representations. Currently, there are only visualizations that can be used for local attributes or specific case representations. However, there is no possibility to visualize similarities between complete processes accordingly so far, although complex domains may be present. Therefore, an extension of existing approaches or the design of new suitable concepts for this application domain is necessary. The contribution of this work is to enable a more profound understanding of similarity for knowledge engineers who create a similarity model and support them in this task by using visualization methods in Process-Oriented Case-Based Reasoning (POCBR). For this purpose, we present related approaches and evaluate them against derived requirements for visualizations in POCBR. On this basis, suitable visualizations are further developed as well as new approaches designed. Three such visualizations are created: (1) a graph mapping approach, (2) a merge graph, and (3) a visualization based on heatmaps. An evaluation of these approaches has been performed based on the requirements in which the domain experts determine the graph-mapping visualization as best-suited for engineering of similarity models.},
  keywords 	   = {Visualization, Explanation, Similarity, Process-Oriented Case-Based Reasoning, Explainable Case-Based Reasoning}
}

Modeling similarity measures in Case-Based Reasoning is a knowledge-intensive, demanding, and error-prone task even for domain experts. Visualizations offer support for users, but are currently only available for certain subdomains and case representations. Currently, there are only visualizations that can be used for local attributes or specific case representations. However, there is no possibility to visualize similarities between complete processes accordingly so far, although complex domains may be present. Therefore, an extension of existing approaches or the design of new suitable concepts for this application domain is necessary. The contribution of this work is to enable a more profound understanding of similarity for knowledge engineers who create a similarity model and support them in this task by using visualization methods in Process-Oriented Case-Based Reasoning (POCBR). For this purpose, we present related approaches and evaluate them against derived requirements for visualizations in POCBR. On this basis, suitable visualizations are further developed as well as new approaches designed. Three such visualizations are created: (1) a graph mapping approach, (2) a merge graph, and (3) a visualization based on heatmaps. An evaluation of these approaches has been performed based on the requirements in which the domain experts determine the graph-mapping visualization as best-suited for engineering of similarity models.

@article{Grger2023,
  doi = {10.1515/itit-2022-0077},
  url = {https://doi.org/10.1515/itit-2022-0077},
  year = {2023},
  volume = {65},
  number = {3},
  month = jun,
  publisher = {Walter de Gruyter {GmbH}},
  author = {Joscha Gr\"{u}ger and Lukas Malburg and Ralph Bergmann},
  title = {{IoT}-enriched event log generation and quality analytics: a case study},
  url          = {http://www.wi2.uni-trier.de/shared/publications/10.1515_itit-2022-0077.pdf},
  journal = {it - Information Technology},
  keywords     = {{Data Quality in Event Logs, DataStream XES Extension, IoT, IoT-Enriched Event Log, Physical Smart Factory, Process Mining}},
  abstract     = {Modern technologies such as the Internet of Things (IoT) are becoming increasingly important in various fields, including business process management (BPM) research. An important area of research in BPM is process mining, which can be used to analyze event logs e.g., to check the conformance of running processes. However, the data ingested in IoT environments often contain data quality issues (DQIs) due to system complexity and sensor heterogeneity, among other factors. To date, however, there has been little work on IoT event logs, DQIs occurring in them, and how to handle them. In this case study, we generate an IoT event log, perform a structured data quality analysis, and describe how we addressed the problems we encountered in pre-processing.}
}

Modern technologies such as the Internet of Things (IoT) are becoming increasingly important in various fields, including business process management (BPM) research. An important area of research in BPM is process mining, which can be used to analyze event logs e.g., to check the conformance of running processes. However, the data ingested in IoT environments often contain data quality issues (DQIs) due to system complexity and sensor heterogeneity, among other factors. To date, however, there has been little work on IoT event logs, DQIs occurring in them, and how to handle them. In this case study, we generate an IoT event log, perform a structured data quality analysis, and describe how we addressed the problems we encountered in pre-processing.

@inproceedings{Brand.2023_RLForAdaptiveWorkflows,
  author       = {Florian Brand and
                  Katharina Lott and
                  Lukas Malburg and
                  Maximilian Hoffmann and
                  Ralph Bergmann},
  editor       = {Lukas Malburg and
                  Deepika Verma},
  title        = {{Using Deep Reinforcement Learning for the Adaptation of Semantic Workflows}},
  booktitle    = {Proceedings of the Workshops at the 31st International Conference
                  on Case-Based Reasoning {(ICCBR-WS} 2023) co-located with the 31st
                  International Conference on Case-Based Reasoning {(ICCBR} 2023), Aberdeen,
                  Scotland, UK, July 17, 2023},
  series       = {{CEUR} Workshop Proceedings},
  volume       = {3438},
  pages        = {55--70},
  publisher    = {CEUR-WS.org},
  year         = {2023},
  keywords     = {{Case-Based Reasoning, Semantic Workflows, Deep Learning, Reinforcement Learning}},
  abstract     = {Case-Based Reasoning (CBR) solves new problems by using experience represented by solved cases. The acquisition of adaptation knowledge and its subsequent application remains a classic challenge for CBR applications. In this paper, we present a novel approach for adapting semantic workflows during the reuse phase of the CBR cycle. A reinforcement learning agent is utilized, which applies different actions to change nodes of the workflow. Thereby, changes to the workflow are made by replacing, deleting or adding nodes. The agent is evaluated in a case study outlining its ability to adapt a semantic graph in a smart manufacturing domain. While the approach is detailed for the application in the particular domain, it can be adopted for the usage in other process-oriented domains.},
  url          = {http://www.wi2.uni-trier.de/shared/publications/2023_Brand_RLForAdaptiveWorkflows.pdf}
}

Case-Based Reasoning (CBR) solves new problems by using experience represented by solved cases. The acquisition of adaptation knowledge and its subsequent application remains a classic challenge for CBR applications. In this paper, we present a novel approach for adapting semantic workflows during the reuse phase of the CBR cycle. A reinforcement learning agent is utilized, which applies different actions to change nodes of the workflow. Thereby, changes to the workflow are made by replacing, deleting or adding nodes. The agent is evaluated in a case study outlining its ability to adapt a semantic graph in a smart manufacturing domain. While the approach is detailed for the application in the particular domain, it can be adopted for the usage in other process-oriented domains.

@inproceedings{Malburg.2023_TimeSeriesInCBR,
  author       = {Lukas Malburg and
                  Alexander Schultheis and
                  Ralph Bergmann},
  editor       = {Lukas Malburg and
                  Deepika Verma},
  title        = {{Modeling and Using Complex IoT Time Series Data in Case-Based Reasoning:
                  From Application Scenarios to Implementations}},
  booktitle    = {Proceedings of the Workshops at the 31st International Conference
                  on Case-Based Reasoning {(ICCBR-WS} 2023) co-located with the 31st
                  International Conference on Case-Based Reasoning {(ICCBR} 2023), Aberdeen,
                  Scotland, UK, July 17, 2023},
  series       = {{CEUR} Workshop Proceedings},
  volume       = {3438},
  pages        = {81--96},
  publisher    = {CEUR-WS.org},
  year         = {2023},
  keywords     = {{Case-Based Reasoning, Temporal Case-Based Reasoning, Internet of Things, Time Series Data, ProCAKE}},
  abstract     = {The research area of Internet of Things (IoT) is gaining more relevance for several domains and application areas, including Case-Based Reasoning (CBR). However, IoT data is characterized by high volumes and variance of data types, making the application of CBR methods difficult. Since only few works have been published in this area so far, the integration and consideration of complex IoT data such as time series data in CBR frameworks is still in its infancy. To catch up with the current state-of-the-art, we present a comprehensive literature review on Temporal Case-Based Reasoning and time series data in CBR as part of our contribution. Furthermore, we present typical application scenarios for using IoT time series data in practice that can be addressed in further research. To build suitable CBR implementations for that purpose, we define a procedure model that can be used for time series data in CBR. In this context, we address the implementation of the application scenarios in the ProCAKE CBR framework.},
  url          = {http://www.wi2.uni-trier.de/shared/publications/2023_MalburgEtAl_TimeSeriesInCBR.pdf}
}

The research area of Internet of Things (IoT) is gaining more relevance for several domains and application areas, including Case-Based Reasoning (CBR). However, IoT data is characterized by high volumes and variance of data types, making the application of CBR methods difficult. Since only few works have been published in this area so far, the integration and consideration of complex IoT data such as time series data in CBR frameworks is still in its infancy. To catch up with the current state-of-the-art, we present a comprehensive literature review on Temporal Case-Based Reasoning and time series data in CBR as part of our contribution. Furthermore, we present typical application scenarios for using IoT time series data in practice that can be addressed in further research. To build suitable CBR implementations for that purpose, we define a procedure model that can be used for time series data in CBR. In this context, we address the implementation of the application scenarios in the ProCAKE CBR framework.

@inproceedings{GrumbachB23,
  author       = {Lisa Grumbach and
                  Ralph Bergmann},
  editor       = {Stewart Massie and
                  Sutanu Chakraborti},
  title        = {A Case-Based Approach for Workflow Flexibility by Deviation},
  booktitle    = {Case-Based Reasoning Research and Development - 31st International
                  Conference, {ICCBR} 2023, Aberdeen, UK, July 17-20, 2023, Proceedings},
  series       = {Lecture Notes in Computer Science},
  volume       = {14141},
  pages        = {294--308},
  publisher    = {Springer},
  year         = {2023},
  url          = {http://www.wi2.uni-trier.de/shared/publications/2023_ICCBR_Grumbach.pdf},
  doi          = {10.1007/978-3-031-40177-0\_19},
  timestamp    = {Sat, 05 Aug 2023 00:01:33 +0200},
  biburl       = {https://dblp.org/rec/conf/iccbr/GrumbachB23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@phdthesis{Grumbach23,
	title        = {{Flexible Workflows - A Constraint- and Case-Based Approach}},
	author       = {Grumbach, Lisa},
	year         = 2023,
	url          = {http://www.wi2.uni-trier.de/shared/publications/2023_Dissertation_Grumbach.pdf},
	school       = {University of Trier, Germany}
}

@proceedings{Malburg.2023_ICCBRWorkshopProceedings,
  editor       = {Lukas Malburg and Deepika Verma},
  title        = {{Proceedings of the Workshops at the 31st International Conference
                  on Case-Based Reasoning {(ICCBR-WS} 2023) co-located with the 31st
                  International Conference on Case-Based Reasoning {(ICCBR} 2023), Aberdeen,
                  Scotland, UK, July 17, 2023}},
  series       = {{CEUR} Workshop Proceedings},
  volume       = {3438},
  publisher    = {CEUR-WS.org},
  year         = {2023},
  url          = {http://sunsite.informatik.rwth-aachen.de/ftp/pub/publications/CEUR-WS/Vol-3438.zip}
}

@inproceedings{Ehrendorfer.2023_IoPTRepository,
  author       = {Matthias Ehrendorfer and
                  Yannis Bertrand and
                  Lukas Malburg and
                  Juergen Mangler and
                  Joscha Gr{\"{u}}ger and
                  Stefanie Rinderle{-}Ma and
                  Ralph Bergmann and
                  Estefan{\'{\i}}a Serral Asensio},
  editor       = {Dirk Fahland and
                  Andr{\'{e}}s Jim{\'{e}}nez{-}Ram{\'{\i}}rez and
                  Akhil Kumar and
                  Jan Mendling and
                  Brian T. Pentland and
                  Stefanie Rinderle{-}Ma and
                  Tijs Slaats and
                  Johan Versendaal and
                  Barbara Weber and
                  Mathias Weske and
                  Karolin Winter},
  title        = {{Internet of Processes and Things: {A} Repository for IoT-Enriched
                  Event Logs in Smart Environments}},
  booktitle    = {{Proceedings of the Best Dissertation Award, Doctoral Consortium, and Demonstration {\&} Resources Forum at {BPM} 2023 co-located with 21st International Conference on Business Process Management {(BPM} 2023), Utrecht, The Netherlands, September 11th to 15th, 2023}},
  series       = {{CEUR} Workshop Proceedings},
  volume       = {3469},
  pages        = {92--96},
  keywords     = {{IoT-Enriched Event Logs, Business Process Management, IoT}},
  abstract     = {Current research efforts from the Business Processes Management and IoT communities, touching novel techniques such as object-centric process mining, predictive process monitoring or IoT based process enhancement suffer from a lack of real-world open data-sets from different domains to evaluate and refine their approaches. In this paper we present an online resource for the collection of IoT-enhanced process logs, hoping to inspire companies, organisations and researchers to cooperate in the release of well-understood high-quality data sets.},
  publisher    = {CEUR-WS.org},
  year         = {2023},
  url          = {http://www.wi2.uni-trier.de/shared/publications/2023_Ehrendorfer_IoPTRepository.pdf}
}

Current research efforts from the Business Processes Management and IoT communities, touching novel techniques such as object-centric process mining, predictive process monitoring or IoT based process enhancement suffer from a lack of real-world open data-sets from different domains to evaluate and refine their approaches. In this paper we present an online resource for the collection of IoT-enhanced process logs, hoping to inspire companies, organisations and researchers to cooperate in the release of well-understood high-quality data sets.

@inproceedings{Malburg.2023_AdaptiveWorkflowsByCBRAndPlanning,
	title        = {{Adaptive Management of Cyber-Physical Workflows by Means of Case-Based Reasoning and Automated Planning}},
	author       = {Lukas Malburg and Florian Brand and Ralph Bergmann},
	year         = 2023,
	booktitle    = {Enterprise Design, Operations, and Computing (EDOC) Workshops 2022},
	publisher    = {Springer.},
	series       = {Lecture Notes in Business Information Processing},
	volume       = 466,
	pages        = {79--95},
	doi          = {10.1007/978-3-031-26886-1\_5},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2023_EDOC_MalburgEtAl_AdaptiveWorkflows_by_CBR_and_Planning.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Sales, Tiago Prince and Proper, Henderik A. and Guizzardi, Giancarlo and Montali, Marco and Maggi, Fabrizio Maria and Fonseca, Claudenir M.},
	keywords     = {{Case-Based Reasoning, Automated Planning, Industry 4.0, Adaptive Workflow Management, Cyber-Physical Workflows}},
	abstract     = {Today, it is difficult for companies to react to unforeseen events, e.g., global crises. Highly standardized manufacturing processes are particularly limited in their ability to react flexibly, creating a demand for more advanced workflow management techniques, e.g., extended by artificial intelligence methods. In this paper, we describe how Case-Based Reasoning (CBR) can be combined with automated planning to enhance flexibility in cyber-physical production workflows. We present a compositional adaptation method complemented with generative adaptation to resolve unexpected situations during workflow execution. This synergy is advantageous since CBR provides specific knowledge about already experienced situations, whereas planning assists with general knowledge about the domain. In an experimental evaluation, we show that CBR offers a good basis by reusing cases and by adapting them to better suit the current problem. The combination with automated planning further improves these results and, thus, contributes to enhance the flexibility of cyber-physical workflows.}
}

Today, it is difficult for companies to react to unforeseen events, e.g., global crises. Highly standardized manufacturing processes are particularly limited in their ability to react flexibly, creating a demand for more advanced workflow management techniques, e.g., extended by artificial intelligence methods. In this paper, we describe how Case-Based Reasoning (CBR) can be combined with automated planning to enhance flexibility in cyber-physical production workflows. We present a compositional adaptation method complemented with generative adaptation to resolve unexpected situations during workflow execution. This synergy is advantageous since CBR provides specific knowledge about already experienced situations, whereas planning assists with general knowledge about the domain. In an experimental evaluation, we show that CBR offers a good basis by reusing cases and by adapting them to better suit the current problem. The combination with automated planning further improves these results and, thus, contributes to enhance the flexibility of cyber-physical workflows.

@article{Malburg_MAPEK_Loops_2023,
	title        = {{Applying MAPE-K control loops for adaptive workflow management in smart factories}},
	author       = {Lukas Malburg and Maximilian Hoffmann and Ralph Bergmann},
	year         = 2023,
	journal      = {{Journal of Intelligent Information Systems}},
	pages		 = {83--111},
	volume       = {61},
	number       = {1},
	doi          = {10.1007/s10844-022-00766-w},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2023_MalburgEtAl_MAPEK_Loops.pdf},
	keywords     = {{Complex event processing, Automated planning, Cyber-physical environments, Smart factories, Adaptive workflow management, Process adaptation}},
	abstract     = {Monitoring the state of currently running processes and reacting to ad-hoc situations during runtime is a key challenge in Business Process Management (BPM). This is especially the case in cyber-physical environments that are characterized by high context sensitivity. MAPE-K control loops are widely used for self-management in these environments and describe four phases for approaching this challenge: Monitor, Analyze, Plan, and Execute. In this paper, we present an architectural solution as well as implementation proposals for using MAPE-K control loops for adaptive workflow management in smart factories. We use Complex Event Processing (CEP) techniques and the process execution states of a Workflow Management System (WfMS) in the monitoring phase. In addition, we apply automated planning techniques to resolve detected exceptional situations and to continue process execution. The experimental evaluation with a physical smart factory shows the potential of the developed approach that is able to detect failures by using IoT sensor data and to resolve them autonomously in near real time with considerable results.}
}

Monitoring the state of currently running processes and reacting to ad-hoc situations during runtime is a key challenge in Business Process Management (BPM). This is especially the case in cyber-physical environments that are characterized by high context sensitivity. MAPE-K control loops are widely used for self-management in these environments and describe four phases for approaching this challenge: Monitor, Analyze, Plan, and Execute. In this paper, we present an architectural solution as well as implementation proposals for using MAPE-K control loops for adaptive workflow management in smart factories. We use Complex Event Processing (CEP) techniques and the process execution states of a Workflow Management System (WfMS) in the monitoring phase. In addition, we apply automated planning techniques to resolve detected exceptional situations and to continue process execution. The experimental evaluation with a physical smart factory shows the potential of the developed approach that is able to detect failures by using IoT sensor data and to resolve them autonomously in near real time with considerable results.

@article{Malburg_ConvertingSWSToPDDL_2023,
	title = {Converting semantic web services into formal planning domain descriptions to enable manufacturing process planning and scheduling in industry 4.0},
	journal = {Engineering Applications of Artificial Intelligence},
	volume = {126},
	pages = {106727},
	year = {2023},
	issn = {0952-1976},
	doi = {10.1016/j.engappai.2023.106727},
	author = {Lukas Malburg and Patrick Klein and Ralph Bergmann},
	keywords = {Semantic web services, Industry 4.0, Automated planning, Planning domain definition language, Cyber-physical workflows},
	abstract = {To build intelligent manufacturing systems that react flexibly in case of failures or unexpected circumstances, manufacturing capabilities of production systems must be utilized as much as possible. Artificial Intelligence (AI) and, in particular, automated planning can contribute to this by enabling flexible production processes. To efficiently leverage automated planning, an almost complete planning domain description of the real-world is necessary. However, creating such planning descriptions is a demanding and error-prone task that requires high manual efforts even for domain experts. In addition, maintaining the encoded knowledge is laborious and, thus, can lead to outdated domain descriptions. To reduce the high efforts, already existing knowledge can be reused and transformed automatically into planning descriptions to benefit from organization-wide knowledge engineering activities. This paper presents a novel approach that reduces the described efforts by reusing existing knowledge for planning and scheduling in Industry 4.0 (I4.0). For this purpose, requirements for developing a converter that transforms existing knowledge are derived from literature. Based on these requirements, the SWS2PDDL converter is developed that transforms the knowledge into formal Planning Domain Definition Language (PDDL) descriptions. The approach’s usefulness is verified by a practical evaluation with a near real-world application scenario by generating failures in a physical smart factory and evaluating the generated re-planned production processes. When comparing the resulting plan quality to those achieved by using a manually modeled planning domain by a domain expert, the automatic transformation by SWS2PDDL leads to comparable or even better results without requiring the otherwise high manual modeling efforts.},
	url = {http://www.wi2.uni-trier.de/shared/publications/2023_Malburg_ConvertingSemanticServicesToPDDL.pdf}
}

To build intelligent manufacturing systems that react flexibly in case of failures or unexpected circumstances, manufacturing capabilities of production systems must be utilized as much as possible. Artificial Intelligence (AI) and, in particular, automated planning can contribute to this by enabling flexible production processes. To efficiently leverage automated planning, an almost complete planning domain description of the real-world is necessary. However, creating such planning descriptions is a demanding and error-prone task that requires high manual efforts even for domain experts. In addition, maintaining the encoded knowledge is laborious and, thus, can lead to outdated domain descriptions. To reduce the high efforts, already existing knowledge can be reused and transformed automatically into planning descriptions to benefit from organization-wide knowledge engineering activities. This paper presents a novel approach that reduces the described efforts by reusing existing knowledge for planning and scheduling in Industry 4.0 (I4.0). For this purpose, requirements for developing a converter that transforms existing knowledge are derived from literature. Based on these requirements, the SWS2PDDL converter is developed that transforms the knowledge into formal Planning Domain Definition Language (PDDL) descriptions. The approach’s usefulness is verified by a practical evaluation with a near real-world application scenario by generating failures in a physical smart factory and evaluating the generated re-planned production processes. When comparing the resulting plan quality to those achieved by using a manually modeled planning domain by a domain expert, the automatic transformation by SWS2PDDL leads to comparable or even better results without requiring the otherwise high manual modeling efforts.

@article{Guldner_AICPPS_2023,
	url = {https://doi.org/10.1515/itit-2023-0001},
	title = {A framework for AI-based self-adaptive cyber-physical process systems},
	author = {Achim Guldner and Maximilian Hoffmann and Christian Lohr and Rüdiger Machhamer and Lukas Malburg and Marlies Morgen and Stephanie C. Rodermund and Florian Schäfer and Lars Schaupeter and Jens Schneider and Felix Theusch and Ralph Bergmann and Guido Dartmann and Norbert Kuhn and Stefan Naumann and Ingo J. Timm and Matthias Vette-Steinkamp and Benjamin Weyers},
	journal = {it - Information Technology},
	volume = {65},
	number = {3},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2023_Guldner_AICPPS.pdf},
	doi = {doi:10.1515/itit-2023-0001},
	keywords     = {{Artificial Intelligence, Business Process Managemenet, Cyber-Physical Systems, Framework, Green AI, Process-Aware Information System}},
	abstract     = {{Digital transformation is both an opportunity and a challenge. To take advantage of this opportunity for humans and the environment, the transformation process must be understood as a design process that affects almost all areas of life. In this paper, we investigate AI-Based Self-Adaptive Cyber-Physical Process Systems (AI-CPPS) as an extension of the traditional CPS view. As contribution, we present a framework that addresses challenges that arise from recent literature. The aim of the AI-CPPS framework is to enable an adaptive integration of IoT environments with higher-level process-oriented systems. In addition, the
framework integrates humans as actors into the system, which is often neglected by recent related approaches. The framework consists of three layers, i.e., processes, semantic modeling, and systems and actors, and we describe for each layer challenges and solution outlines for application. We also address the requirement to enable the integration of new networked devices under the premise of a targeted process that is optimally designed for humans, while profitably integrating AI and IoT. It is expected that AI-CPPS can contribute significantly to increasing sustainability and quality of life and offer solutions to pressing problems such as environmental protection, mobility, or demographic change. Thus, it is all the more important that the systems themselves do not become a driver of resource consumption.}},
	year = {2023}
}

Digital transformation is both an opportunity and a challenge. To take advantage of this opportunity for humans and the environment, the transformation process must be understood as a design process that affects almost all areas of life. In this paper, we investigate AI-Based Self-Adaptive Cyber-Physical Process Systems (AI-CPPS) as an extension of the traditional CPS view. As contribution, we present a framework that addresses challenges that arise from recent literature. The aim of the AI-CPPS framework is to enable an adaptive integration of IoT environments with higher-level process-oriented systems. In addition, the framework integrates humans as actors into the system, which is often neglected by recent related approaches. The framework consists of three layers, i.e., processes, semantic modeling, and systems and actors, and we describe for each layer challenges and solution outlines for application. We also address the requirement to enable the integration of new networked devices under the premise of a targeted process that is optimally designed for humans, while profitably integrating AI and IoT. It is expected that AI-CPPS can contribute significantly to increasing sustainability and quality of life and offer solutions to pressing problems such as environmental protection, mobility, or demographic change. Thus, it is all the more important that the systems themselves do not become a driver of resource consumption.

@article{Mangler_DataStreamExtension_2023,
	title        = {{DataStream XES Extension: Embedding IoT Sensor Data into Extensible Event Stream Logs}},
	author       = {Mangler, Juergen and Grüger, Joscha and Malburg, Lukas and Ehrendorfer, Matthias and Bertrand, Yannis and Benzin, Janik-Vasily and Rinderle-Ma, Stefanie and Serral Asensio, Estefania and Bergmann, Ralph},
	year         = 2023,
	journal      = {Future Internet},
	volume       = 15,
	number       = 3,
	doi          = {10.3390/fi15030109},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2023_ManglerEtAl_DataStreamExtension.pdf},
	abstract     = {{The Internet of Things (IoT) has been shown to be very valuable for Business Process Management (BPM), for example, to better track and control process executions. While IoT actuators can automatically trigger actions, IoT sensors can monitor the changes in the environment and the humans involved in the processes. These sensors produce large amounts of discrete and continuous data streams, which hold the key to understanding the quality of the executed processes. However, to enable this understanding, it is needed to have a joint representation of the data generated by the process engine executing the process, and the data generated by the IoT sensors. In this paper, we present an extension of the event log standard format XES called DataStream. DataStream enables the connection of IoT data to process events, preserving the full context required for data analysis, even when scenarios or hardware artifacts are rapidly changing. The DataStream extension is designed based on a set of goals and evaluated by creating two datasets for real-world scenarios from the transportation/logistics and manufacturing domains.}},
	keywords     = {{Process Management, Industry 4.0, IoT data, Process Mining, XES}}
}

The Internet of Things (IoT) has been shown to be very valuable for Business Process Management (BPM), for example, to better track and control process executions. While IoT actuators can automatically trigger actions, IoT sensors can monitor the changes in the environment and the humans involved in the processes. These sensors produce large amounts of discrete and continuous data streams, which hold the key to understanding the quality of the executed processes. However, to enable this understanding, it is needed to have a joint representation of the data generated by the process engine executing the process, and the data generated by the IoT sensors. In this paper, we present an extension of the event log standard format XES called DataStream. DataStream enables the connection of IoT data to process events, preserving the full context required for data analysis, even when scenarios or hardware artifacts are rapidly changing. The DataStream extension is designed based on a set of goals and evaluated by creating two datasets for real-world scenarios from the transportation/logistics and manufacturing domains.

@inproceedings{pauli_transfer_learning_2023,
	title        = {{Similarity-Based Retrieval in Process-Oriented Case-Based Reasoning Using Graph Neural Networks and Transfer Learning}},
	author       = {Pauli, Johannes and Hoffmann, Maximilian and Bergmann, Ralph},
	year         = 2023,
	booktitle    = {Proceedings of the 36th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2023, Clearwater Beach, Florida, USA},
	doi          = {10.32473/flairs.36.133040},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2023_Pauli_FLAIRS.pdf},
	abstract     = {Similarity-based retrieval of semantic graphs is a crucial task of Process-Oriented Case-Based Reasoning (POCBR) that is usually complex and time-consuming, as it requires some kind of inexact graph matching. Previous work tackles this problem by using Graph Neural Networks (GNNs) to learn pairwise graph similarities. In this paper, we present a novel approach that improves on the GNN-based case retrieval with a Transfer Learning (TL) setup, composed of two phases: First, the pretraining phase trains a model for assessing the similarities between graph nodes and edges and their semantic annotations. Second, the pretrained model is then integrated into the GNN model by either using fine-tuning, i.e., the parameters of the pretrained model are further trained, or feature extraction, i.e., the parameters of the pretrained model are converted to constants. The experimental evaluation examines the quality and performance of the models based on TL compared to the GNN models from previous work for three semantic graph domains with various properties. The results show the great potential of the proposed approach for reducing the similarity prediction error and the training time.}
}

Similarity-based retrieval of semantic graphs is a crucial task of Process-Oriented Case-Based Reasoning (POCBR) that is usually complex and time-consuming, as it requires some kind of inexact graph matching. Previous work tackles this problem by using Graph Neural Networks (GNNs) to learn pairwise graph similarities. In this paper, we present a novel approach that improves on the GNN-based case retrieval with a Transfer Learning (TL) setup, composed of two phases: First, the pretraining phase trains a model for assessing the similarities between graph nodes and edges and their semantic annotations. Second, the pretrained model is then integrated into the GNN model by either using fine-tuning, i.e., the parameters of the pretrained model are further trained, or feature extraction, i.e., the parameters of the pretrained model are converted to constants. The experimental evaluation examines the quality and performance of the models based on TL compared to the GNN models from previous work for three semantic graph domains with various properties. The results show the great potential of the proposed approach for reducing the similarity prediction error and the training time.

@inproceedings{hoffmann_ltr_2023,
	title        = {{Ranking-Based Case Retrieval with Graph Neural Networks in Process-Oriented Case-Based Reasoning}},
	author       = {Hoffmann, Maximilian and Bergmann, Ralph},
	year         = 2023,
	booktitle    = {Proceedings of the 36th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2023, Clearwater Beach, Florida, USA},
	doi          = {10.32473/flairs.36.133039},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2023_Hoffmann_FLAIRS.pdf},
	abstract     = {In Process-Oriented Case-Based Reasoning (POCBR), experiential knowledge from previous problem-solving situations is retrieved from a case base to be reused for upcoming problems. The task of retrieval is approached in previous work by using Graph Neural Networks (GNNs) to learn workflow similarities which are, in turn, used to find similar workflows w.r.t. a query workflow. This paper is motivated by the fact that these GNNs are mostly used for predicting the similarity between two workflows (query and case), while the retrieval in CBR is only concerned with the ranking of the most similar workflows from the case base w.r.t. the query. Thus, we propose a novel approach to extend the GNN-based workflow retrieval by a Learning-to-Rank (LTR) component where rankings instead of similarities between cases are predicted. The main contribution of this paper addresses the changes to the GNNs from previous work, such that their model architecture predicts pairwise preferences between cases w.r.t. a query and that they can be trained using labeled preference data. In order to transform these preferences into a case ranking, we also describe rank aggregation methods with different levels of computational complexity. The experimental evaluation compares different models for predicting similarities and rankings in case retrieval scenarios. The results indicate the potential of our ranking-based approach in significantly improving retrieval quality with only small impacts on the performance.}
}

In Process-Oriented Case-Based Reasoning (POCBR), experiential knowledge from previous problem-solving situations is retrieved from a case base to be reused for upcoming problems. The task of retrieval is approached in previous work by using Graph Neural Networks (GNNs) to learn workflow similarities which are, in turn, used to find similar workflows w.r.t. a query workflow. This paper is motivated by the fact that these GNNs are mostly used for predicting the similarity between two workflows (query and case), while the retrieval in CBR is only concerned with the ranking of the most similar workflows from the case base w.r.t. the query. Thus, we propose a novel approach to extend the GNN-based workflow retrieval by a Learning-to-Rank (LTR) component where rankings instead of similarities between cases are predicted. The main contribution of this paper addresses the changes to the GNNs from previous work, such that their model architecture predicts pairwise preferences between cases w.r.t. a query and that they can be trained using labeled preference data. In order to transform these preferences into a case ranking, we also describe rank aggregation methods with different levels of computational complexity. The experimental evaluation compares different models for predicting similarities and rankings in case retrieval scenarios. The results indicate the potential of our ranking-based approach in significantly improving retrieval quality with only small impacts on the performance.

@article{Grueger2022Reconstructing,
  title={{Reconstructing invisible deviating events: A conformance checking approach for recurring events}},
  author={Gr{\"u}ger, Joscha and Kuhn, Martin and Bergmann, Ralph},
  journal={Mathematical Biosciences and Engineering},
  volume={19},
  number={11},
  pages={11782--11799},
  year={2022},
  month={Aug},
  doi={10.3934/mbe.2022549},
  publisher={AIMS Press},
  abstract={Conformance checking enables organizations to determine whether their executed processes are compliant with the intended process. However, if the processes contain recurring activities, state-of-the-art approaches unfortunately have difficulties calculating the conformance. The occurrence of complex temporal rules can further increase the complexity of the problem. Identifying this limitation, this paper presents a novel approach towards dealing with recurring activities in conformance checking. The core idea of the approach is to reconstruct the missing events in the event log using defined rules while incorporating specified temporal event characteristics. This approach then enables the use of native conformance checking algorithms. The paper illustrates the algorithmic approach and defines the required temporal event characteristics. Furthermore, the approach is applied and evaluated in a case study on an event log for melanoma surveillance.}
}

Conformance checking enables organizations to determine whether their executed processes are compliant with the intended process. However, if the processes contain recurring activities, state-of-the-art approaches unfortunately have difficulties calculating the conformance. The occurrence of complex temporal rules can further increase the complexity of the problem. Identifying this limitation, this paper presents a novel approach towards dealing with recurring activities in conformance checking. The core idea of the approach is to reconstruct the missing events in the event log using defined rules while incorporating specified temporal event characteristics. This approach then enables the use of native conformance checking algorithms. The paper illustrates the algorithmic approach and defines the required temporal event characteristics. Furthermore, the approach is applied and evaluated in a case study on an event log for melanoma surveillance.

@inproceedings{GruegerGJB2022,
  author       = {Joscha Gr{\"{u}}ger and
                  Tobias Geyer and
                  David Jilg and
                  Ralph Bergmann},
  editor       = {Marco Montali and
                  Arik Senderovich and
                  Matthias Weidlich},
  title        = {{SAMPLE:} {A} Semantic Approach for Multi-perspective Event Log Generation},
  booktitle    = {Process Mining Workshops - {ICPM} 2022 International Workshops, Bozen-Bolzano,
                  Italy, October 23-28, 2022, Revised Selected Papers},
  series       = {Lecture Notes in Business Information Processing},
  volume       = {468},
  pages        = {328--340},
  publisher    = {Springer Nature Switzerland},
  year         = {2022},
  url          = {https://link.springer.com/chapter/10.1007/978-3-031-27815-0_24},
  doi          = {10.1007/978-3-031-27815-0\_24},
  timestamp    = {Fri, 26 May 2023 07:40:33 +0200},
  biburl       = {https://dblp.org/rec/conf/icpm/GrugerGJB22.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org},
  abstract = {Data and process mining techniques can be applied in many areas to gain valuable insights. For many reasons, accessibility to real-world business and medical data is severely limited. However, research, but especially the development of new methods, depends on a sufficient basis of realistic data. Due to the lack of data, this progress is hindered. This applies in particular to domains that use personal data, such as healthcare. With adequate quality, synthetic data can be a solution to this problem. In the procedural field, some approaches have already been presented that generate synthetic data based on a process model. However, only a few have included the data perspective so far. Data semantics, which is crucial for the quality of the generated data, has not yet been considered. Therefore, in this paper we present the multi-perspective event log generation approach SAMPLE that considers the data perspective and, in particular, its semantics. The evaluation of the approach is based on a process model for the treatment of malignant melanoma. As a result, we were able to integrate the semantic of data into the log generation process and identify new challenges.",
isbn="978-3-031-27815-0}
}

Data and process mining techniques can be applied in many areas to gain valuable insights. For many reasons, accessibility to real-world business and medical data is severely limited. However, research, but especially the development of new methods, depends on a sufficient basis of realistic data. Due to the lack of data, this progress is hindered. This applies in particular to domains that use personal data, such as healthcare. With adequate quality, synthetic data can be a solution to this problem. In the procedural field, some approaches have already been presented that generate synthetic data based on a process model. However, only a few have included the data perspective so far. Data semantics, which is crucial for the quality of the generated data, has not yet been considered. Therefore, in this paper we present the multi-perspective event log generation approach SAMPLE that considers the data perspective and, in particular, its semantics. The evaluation of the approach is based on a process model for the treatment of malignant melanoma. As a result, we were able to integrate the semantic of data into the log generation process and identify new challenges.", isbn="978-3-031-27815-0

@proceedings{Bergmann.2022_KIProceedings,
  editor       = {Ralph Bergmann and Lukas Malburg and Stephanie C. Rodermund and Ingo J. Timm},
  title        = {{KI 2022: Advances in Artificial Intelligence - 45th German Conference
                  on AI, Trier, Germany, September 19-23, 2022, Proceedings}},
  series       = {Lecture Notes in Computer Science},
  volume       = {13404},
  publisher    = {Springer},
  year         = {2022},
  url          = {http://www.wi2.uni-trier.de/shared/publications/FrontMatter_KI_2022.pdf},
  doi          = {10.1007/978-3-031-15791-2},
  isbn         = {978-3-031-15790-5}
}

@inproceedings{hoffmann_hyperparameters_2022,
	title        = {{Improving Automated Hyperparameter Optimization with Case-Based Reasoning}},
	author       = {Maximilian Hoffmann and Ralph Bergmann},
	year         = 2022,
	booktitle    = {Case-Based Reasoning Research and Development - 30th International Conference, {ICCBR} 2022, Nancy, France, September 12-15, 2022, Proceedings},
	publisher    = {Springer},
	series       = {Lecture Notes in Computer Science},
	volume       = 13405,
	pages        = {273--288},
	doi          = {10.1007/978-3-031-14923-8\_18},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2022_ICCBR__Hyperparameter_Optimization_with_CBR.pdf},
	editor       = {Mark T. Keane and Nirmalie Wiratunga},
	abstract     = {The hyperparameter configuration of machine learning models has a great influence on their performance. These hyperparameters are often set either manually w. r. t. to the experience of an expert or by an Automated Hyperparameter Optimization (HPO) method. However, integrating experience knowledge into HPO methods is challenging. Therefore, we propose the approach HypOCBR (Hyperparameter Optimization with Case-Based Reasoning) that uses Case-Based Reasoning (CBR) to improve the optimization of hyperparameters. HypOCBR is used as an addition to HPO methods and builds up a case base of sampled hyperparameter vectors with their loss values. The case base is then used to retrieve hyperparameter vectors given a query vector and to make decisions whether to proceed trialing with this query or abort and sample another vector. The experimental evaluation investigates the suitability of HypOCBR for two deep learning setups of varying complexity. It shows its potential to improve the optimization results, especially in complex scenarios with limited optimization time.}
}

The hyperparameter configuration of machine learning models has a great influence on their performance. These hyperparameters are often set either manually w. r. t. to the experience of an expert or by an Automated Hyperparameter Optimization (HPO) method. However, integrating experience knowledge into HPO methods is challenging. Therefore, we propose the approach HypOCBR (Hyperparameter Optimization with Case-Based Reasoning) that uses Case-Based Reasoning (CBR) to improve the optimization of hyperparameters. HypOCBR is used as an addition to HPO methods and builds up a case base of sampled hyperparameter vectors with their loss values. The case base is then used to retrieve hyperparameter vectors given a query vector and to make decisions whether to proceed trialing with this query or abort and sample another vector. The experimental evaluation investigates the suitability of HypOCBR for two deep learning setups of varying complexity. It shows its potential to improve the optimization results, especially in complex scenarios with limited optimization time.

@inproceedings{hoffmann_gpu_astar_2022,
	title        = {{GPU-Based Graph Matching for Accelerating Similarity Assessment in Process-Oriented Case-Based Reasoning}},
	author       = {Maximilian Hoffmann and Lukas Malburg and Nico Bach and Ralph Bergmann},
	year         = 2022,
	booktitle    = {Case-Based Reasoning Research and Development - 30th International Conference, {ICCBR} 2022, Nancy, France, September 12-15, 2022, Proceedings},
	publisher    = {Springer},
	series       = {Lecture Notes in Computer Science},
	volume       = 13405,
	pages        = {240--255},
	doi          = {10.1007/978-3-031-14923-8\_16},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2022_ICCBR__A_Star_GPU.pdf},
	editor       = {Mark T. Keane and Nirmalie Wiratunga},
	abstract     = {In Process-Oriented Case-Based Reasoning (POCBR), determining the similarity between cases represented as semantic graphs often requires some kind of inexact graph matching, which generally is an NP-hard problem. Heuristic search algorithms such as A* search have been successfully applied for this task, but the computational performance is still a limiting factor for large case bases. As related work shows a great potential for accelerating A* search by using GPUs, we propose a novel approach called AMonG for efficiently computing graph similarities with an A* graph matching process involving GPU computing. The three-phased matching process distributes the search process over multiple search instances running in parallel on the GPU. We develop and examine different strategies within these phases that allow to customize the matching process adjusted to the problem situation to be solved. The experimental evaluation compares the proposed GPU-based approach with a pure CPU-based one. The results clearly demonstrate that the GPU-based approach significantly outperforms the CPU-based approach in a retrieval scenario, leading to an average speedup factor of 16.}
}

In Process-Oriented Case-Based Reasoning (POCBR), determining the similarity between cases represented as semantic graphs often requires some kind of inexact graph matching, which generally is an NP-hard problem. Heuristic search algorithms such as A* search have been successfully applied for this task, but the computational performance is still a limiting factor for large case bases. As related work shows a great potential for accelerating A* search by using GPUs, we propose a novel approach called AMonG for efficiently computing graph similarities with an A* graph matching process involving GPU computing. The three-phased matching process distributes the search process over multiple search instances running in parallel on the GPU. We develop and examine different strategies within these phases that allow to customize the matching process adjusted to the problem situation to be solved. The experimental evaluation compares the proposed GPU-based approach with a pure CPU-based one. The results clearly demonstrate that the GPU-based approach significantly outperforms the CPU-based approach in a retrieval scenario, leading to an average speedup factor of 16.

@inproceedings{Malburg_AdaptiveWorkflowManagement_2022,
	title        = {{Towards Adaptive Workflow Management by Case-Based Reasoning and Automated Planning}},
	author       = {Malburg, Lukas and Bergmann, Ralph},
	year         = 2022,
	booktitle    = {{Workshops Proceedings for the Thirtieth International Conference on Case-Based Reasoning co-located with the Thirtieth International Conference on Case-Based Reasoning {(ICCBR} 2022), Nancy, France, September 12-15, 2022}},
	publisher    = {CEUR-WS.org},
	volume       = {3389},
	pages        = {211--220},
	series       = {{CEUR} Workshop Proceedings},
	editor       = {Pascal Reuss and Jakob Schönborn},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2023_Malburg_ICCBR.pdf},
	keywords     = {{Case-Based Reasoning, Automated Planning, Industry 4.0, Adaptive Workflow Management, Cyber-Physical Workflows}},
	abstract     = {Adaptive workflow management is an important topic in recent years, as increasing dynamics due to growing customer demands and faster changing market conditions require more flexibility in workflows. This is especially the case for rigid and rather standardized production processes that cannot be easily modified, if, for example, a breakdown occurs in one of the production lines. The goal of the Fourth Industrial Revolution (Industry 4.0), is to provide, among others, more flexible and cost-effective processes in companies by using Artificial Intelligence (AI) methods. In this paper, we present 1) a framework for adaptive workflow management for IoT-enhanced manufacturing processes and 2) an idea for a new adaptation method that combines Case-Based Reasoning (CBR) and automated planning. In this context, we discuss the benefits of such a synergistic combination and introduce the framework and the individual phases according to the 4R (Retrieve, Reuse, Revise, Retain) CBR cycle. In addition, we present our physical factory simulation model that can be used to evaluate the suitability of developed research artifacts.}
}

Adaptive workflow management is an important topic in recent years, as increasing dynamics due to growing customer demands and faster changing market conditions require more flexibility in workflows. This is especially the case for rigid and rather standardized production processes that cannot be easily modified, if, for example, a breakdown occurs in one of the production lines. The goal of the Fourth Industrial Revolution (Industry 4.0), is to provide, among others, more flexible and cost-effective processes in companies by using Artificial Intelligence (AI) methods. In this paper, we present 1) a framework for adaptive workflow management for IoT-enhanced manufacturing processes and 2) an idea for a new adaptation method that combines Case-Based Reasoning (CBR) and automated planning. In this context, we discuss the benefits of such a synergistic combination and introduce the framework and the individual phases according to the 4R (Retrieve, Reuse, Revise, Retain) CBR cycle. In addition, we present our physical factory simulation model that can be used to evaluate the suitability of developed research artifacts.

@inproceedings{Zeyen_myRPA_2022,
	title        = {{Towards Experience-based Assistance for Personal Robotic Process Automation by Process-Oriented Case-based Reasoning}},
	author       = {Zeyen, Christian and Koch, Rudolf, and Schwarz, Sven and Maus, Heiko and Bergmann, Ralph},
	year         = 2022,
	booktitle    = {{ICCBR POCBR’22: Workshop on Process-Oriented Case-based Reasoning at ICCBR-2022, September 2022, Nancy, France}},
	publisher    = {CEUR-WS.org},
	series       = {{CEUR} Workshop Proceedings},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2022_ZeyenEtal_myRPA_ICCBR.pdf},
	editor       = {Pascal Reuss and Jakob Schönborn}
}

@inproceedings{Mathew_FlexiTeams_2022,
	title        = {{FlexiTeam: Flexible Team andWork Organization using Process-Oriented Case-Based Reasoning}},
	author       = {Mathew, Ditty and Bergmann, Ralph and Weyers, Benjamin and Ellwart, Thomas and Bohrmann, Dominique and Hölzchen, Ericson},
	year         = 2022,
	booktitle    = {{ICCBR POCBR’22: Workshop on Process-Oriented Case-based Reasoning at ICCBR-2022, September 2022, Nancy, France}},
	publisher    = {CEUR-WS.org},
	series       = {{CEUR} Workshop Proceedings},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2022_Mathewetal_ICCBR.pdf},
	editor       = {Pascal Reuss and Jakob Schönborn}
}

@inproceedings{Hoffmann.2022_ProGAN,
	title        = {{ProGAN: Toward a Framework for Process Monitoring and Flexibility by Change via Generative Adversarial Networks}},
	author       = {Maximilian Hoffmann and Lukas Malburg and Ralph Bergmann},
	year         = 2022,
	booktitle    = {Business Process Management Workshops - {BPM} 2021, Rome, Italy, September 6 - 10, 2021},
	publisher    = {Springer.},
	series       = {Lecture Notes in Business Information Processing},
	volume       = 436,
	pages        = {43--55},
	doi          = {10.1007/978-3-030-94343-1\_4},
	url          = {https://doi.org/10.1007/978-3-030-94343-1\_4},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2022_AI4BPM_HoffmannEtal_ProGAN.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Andrea Marrella and Barbara Weber},
	keywords     = {{Business process prediction, Generative Adversarial Networks, Flexibility by change, Process adaptation}},
	abstract     = {Monitoring the state of currently running processes and reacting to deviations during runtime is a key challenge in Business Process Management (BPM). The MAPE-K control loop describes four phases for approaching this challenge: Monitor, Analyze, Plan, Execute. In this paper, we present the ProGAN framework, an idea of an approach for implementing the monitor, analyze, and plan phases of MAPE-K. For this purpose, we leverage a deep learning architecture that builds upon Generative Adversarial Networks (GANs): The discriminator is used for monitoring the process in its environment by using sensor data and for detecting deviations w.r.t. the desired process state (monitor phase). The generator is used afterwards for analyzing the detected deviation and its symptoms as well as for adapting the current process to resolve the deviation and to restore the desired state. Both components are trained together by utilizing each other's feedback in a self-supervised way. We demonstrate the application of our approach for an exemplary scenario in the manufacturing domain.}
}

Monitoring the state of currently running processes and reacting to deviations during runtime is a key challenge in Business Process Management (BPM). The MAPE-K control loop describes four phases for approaching this challenge: Monitor, Analyze, Plan, Execute. In this paper, we present the ProGAN framework, an idea of an approach for implementing the monitor, analyze, and plan phases of MAPE-K. For this purpose, we leverage a deep learning architecture that builds upon Generative Adversarial Networks (GANs): The discriminator is used for monitoring the process in its environment by using sensor data and for detecting deviations w.r.t. the desired process state (monitor phase). The generator is used afterwards for analyzing the detected deviation and its symptoms as well as for adapting the current process to resolve the deviation and to restore the desired state. Both components are trained together by utilizing each other's feedback in a self-supervised way. We demonstrate the application of our approach for an exemplary scenario in the manufacturing domain.

@article{Hoffmann.2022_GraphEmbeddingPOCBR,
	title        = {{Using Graph Embedding Techniques in Process-Oriented Case-Based Reasoning}},
	author       = {Maximilian Hoffmann and Ralph Bergmann},
	year         = 2022,
	journal      = {Algorithms},
	volume       = 15,
	number       = 2,
	doi          = {10.3390/a15020027},
	issn         = {1999-4893},
	url          = {https://www.mdpi.com/1999-4893/15/2/27/pdf},
	abstract     = {Similarity-based retrieval of semantic graphs is a core task of Process-Oriented Case-Based Reasoning (POCBR) with applications in real-world scenarios, e.g., in smart manufacturing. The involved similarity computation is usually complex and time-consuming, as it requires some kind of inexact graph matching. To tackle these problems, we present an approach to modeling similarity measures based on embedding semantic graphs via Graph Neural Networks (GNNs). Therefore, we first examine how arbitrary semantic graphs, including node and edge types and their knowledge-rich semantic annotations, can be encoded in a numeric format that is usable by GNNs. Given this, the architecture of two generic graph embedding models from the literature is adapted to enable their usage as a similarity measure for similarity-based retrieval. Thereby, one of the two models is more optimized towards fast similarity prediction, while the other model is optimized towards knowledge-intensive, more expressive predictions. The evaluation examines the quality and performance of these models in preselecting retrieval candidates and in approximating the ground-truth similarities of a graph-matching-based similarity measure for two semantic graph domains. The results show the great potential of the approach for use in a retrieval scenario, either as a preselection model or as an approximation of a graph similarity measure.}
}

Similarity-based retrieval of semantic graphs is a core task of Process-Oriented Case-Based Reasoning (POCBR) with applications in real-world scenarios, e.g., in smart manufacturing. The involved similarity computation is usually complex and time-consuming, as it requires some kind of inexact graph matching. To tackle these problems, we present an approach to modeling similarity measures based on embedding semantic graphs via Graph Neural Networks (GNNs). Therefore, we first examine how arbitrary semantic graphs, including node and edge types and their knowledge-rich semantic annotations, can be encoded in a numeric format that is usable by GNNs. Given this, the architecture of two generic graph embedding models from the literature is adapted to enable their usage as a similarity measure for similarity-based retrieval. Thereby, one of the two models is more optimized towards fast similarity prediction, while the other model is optimized towards knowledge-intensive, more expressive predictions. The evaluation examines the quality and performance of these models in preselecting retrieval candidates and in approximating the ground-truth similarities of a graph-matching-based similarity measure for two semantic graph domains. The results show the great potential of the approach for use in a retrieval scenario, either as a preselection model or as an approximation of a graph similarity measure.

@inproceedings{kumar_dependencyretrieval_2022,
	title        = {{Considering Inter-Case Dependencies During Similarity-Based Retrieval in Process-Oriented Case-Based Reasoning}},
	author       = {Kumar, Rahol and Schultheis, Alexander and Malburg, Lukas and Hoffmann, Maximilian and Bergmann, Ralph},
	year         = 2022,
	booktitle    = {Proceedings of the 35th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2022, Hutchinson Island, Jensen Beach, Florida, USA},
	doi          = {10.32473/flairs.v35i.130680},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2022_Kumar_FLAIRS.pdf},
	abstract     = {In Case-Based Reasoning (CBR), knowledge gained from previously experienced problem-solving situations is stored as cases that can be used to solve similar upcoming problems. Although these cases act as independent knowledge entities, dependencies between cases are common in real-world scenarios, despite being only rarely considered during case retrieval or other CBR phases. In this paper, we introduce so-called inter-case dependencies, which are considered in the context of Process-Oriented CBR (POCBR). Therefore, we 1) derive requirements that must be satisfied for considering dependencies during the retrieval phase, 2) analyze which knowledge representations are suitable for representing dependencies between cases, and, 3) present our approach for Dependency-Guided Retrieval (DGR) that considers these dependencies between cases during the retrieval phase. In the experimental evaluation, the proposed DGR approach is compared to a regular CBR approach in case retrieval scenarios from the cooking domain. The results demonstrate that the use of the DGR approach leads to significantly reduced times for human problem-solving compared to regular CBR.}
}

In Case-Based Reasoning (CBR), knowledge gained from previously experienced problem-solving situations is stored as cases that can be used to solve similar upcoming problems. Although these cases act as independent knowledge entities, dependencies between cases are common in real-world scenarios, despite being only rarely considered during case retrieval or other CBR phases. In this paper, we introduce so-called inter-case dependencies, which are considered in the context of Process-Oriented CBR (POCBR). Therefore, we 1) derive requirements that must be satisfied for considering dependencies during the retrieval phase, 2) analyze which knowledge representations are suitable for representing dependencies between cases, and, 3) present our approach for Dependency-Guided Retrieval (DGR) that considers these dependencies between cases during the retrieval phase. In the experimental evaluation, the proposed DGR approach is compared to a regular CBR approach in case retrieval scenarios from the cooking domain. The results demonstrate that the use of the DGR approach leads to significantly reduced times for human problem-solving compared to regular CBR.

@article{seiger_process_management_smart_factories_2022,
	title        = {{Integrating process management and event processing in smart factories: A systems architecture and use cases}},
	author       = {Seiger, Ronny and Malburg, Lukas and Weber, Barbara and Bergmann, Ralph},
	year         = 2022,
	journal      = {{Journal of Manufacturing Systems}},
	volume       = 63,
	pages        = {575--592},
	doi          = {10.1016/j.jmsy.2022.05.012},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2022_Seiger_JOMS.pdf},
	abstract     = {The developments of new concepts for an increased digitization of manufacturing industries in the context of Industry 4.0 have brought about novel system architectures and frameworks for smart production systems. These range from generic frameworks for Industry 4.0 to domain-specific architectures for Industrial Internet of Things (IIoT). While most of the approaches include a service-based architecture for selective integration with enterprise systems, a close two-way integration of the production control systems and IIoT sensors and actuators with Process-Aware Information Systems (PAIS) on the management level for automation and mining of production processes is rarely discussed. This fusion of Business Process Management (BPM) with IIoT can be mutually beneficial for both research areas, but is still in its infancy. We propose a systems architecture for IIoT that shows how to integrate the low-level hardware components – sensors and actuators – of a smart factory with BPM systems. We discuss the software components and their interactions to address challenges of device encapsulation, integration of sensor events, and interaction with existing BPM systems. This integration is demonstrated within several use cases regarding process modeling, automation and mining for a smart factory model, showing benefits of using BPM technologies to analyze, control, and adapt discrete production processes in IIoT.}
}

The developments of new concepts for an increased digitization of manufacturing industries in the context of Industry 4.0 have brought about novel system architectures and frameworks for smart production systems. These range from generic frameworks for Industry 4.0 to domain-specific architectures for Industrial Internet of Things (IIoT). While most of the approaches include a service-based architecture for selective integration with enterprise systems, a close two-way integration of the production control systems and IIoT sensors and actuators with Process-Aware Information Systems (PAIS) on the management level for automation and mining of production processes is rarely discussed. This fusion of Business Process Management (BPM) with IIoT can be mutually beneficial for both research areas, but is still in its infancy. We propose a systems architecture for IIoT that shows how to integrate the low-level hardware components – sensors and actuators – of a smart factory with BPM systems. We discuss the software components and their interactions to address challenges of device encapsulation, integration of sensor events, and interaction with existing BPM systems. This integration is demonstrated within several use cases regarding process modeling, automation and mining for a smart factory model, showing benefits of using BPM technologies to analyze, control, and adapt discrete production processes in IIoT.

@article{Grueger.2022_SensorStreamExtension,
	title        = {{SensorStream: An XES Extension for Enriching Event Logs with IoT-Sensor Data}},
	author       = {Joscha Grüger and Lukas Malburg and Jürgen Mangler and Yannis Bertrand and Stefanie Rinderle-Ma and Ralph Bergmann and Estefanía {Serral Asensio}},
	year         = 2022,
	journal      = {CoRR},
	volume       = {abs/2206.11392},
	url          = {https://arxiv.org/abs/2206.11392},
	archiveprefix = {arXiv},
	eprint       = {2206.11392},
	abstract     = {Process management and process orchestration/execution are currently hot topics; prevalent trends such as automation and Industry 4.0 require solutions which allow domain-experts to easily model and execute processes in various domains, including manufacturing and health-care. These domains, in turn, rely on a tight integration between hardware and software, i.e. via the Internet of Things (IoT). While process execution is about actuation, i.e. actively triggering actions and awaiting their completion, accompanying IoT sensors monitor humans and the environment. These sensors produce large amounts of procedural, discrete, and continuous data streams, that hold the key to understanding the quality of process subjects (e.g. produced parts), outcome (e.g. quantity and quality), and error causes. Processes constantly evolve in conjunction with their IoT environment. This requires joint storage of data generated by processes, with data generated by the IoT sensors is therefore needed. In this paper, we present an extension of the process log standard format XES, namely SensorStream. SensorStream enables to connect IoT data to process events, as well as a set of semantic annotations to describe the scenario and environment during data collection. This allows to preserve the full context required for data-analysis, so that logs can be analyzed even when scenarios or hardware artifacts are rapidly changing. Through additional semantic annotations, we envision the XES extension log format to be a solid based for the creation of a (semi-)automatic analysis pipeline, which can support domain experts by automatically providing data visualization, or even process insights.}
}

Process management and process orchestration/execution are currently hot topics; prevalent trends such as automation and Industry 4.0 require solutions which allow domain-experts to easily model and execute processes in various domains, including manufacturing and health-care. These domains, in turn, rely on a tight integration between hardware and software, i.e. via the Internet of Things (IoT). While process execution is about actuation, i.e. actively triggering actions and awaiting their completion, accompanying IoT sensors monitor humans and the environment. These sensors produce large amounts of procedural, discrete, and continuous data streams, that hold the key to understanding the quality of process subjects (e.g. produced parts), outcome (e.g. quantity and quality), and error causes. Processes constantly evolve in conjunction with their IoT environment. This requires joint storage of data generated by processes, with data generated by the IoT sensors is therefore needed. In this paper, we present an extension of the process log standard format XES, namely SensorStream. SensorStream enables to connect IoT data to process events, as well as a set of semantic annotations to describe the scenario and environment during data collection. This allows to preserve the full context required for data-analysis, so that logs can be analyzed even when scenarios or hardware artifacts are rapidly changing. Through additional semantic annotations, we envision the XES extension log format to be a solid based for the creation of a (semi-)automatic analysis pipeline, which can support domain experts by automatically providing data visualization, or even process insights.

@article{Malburg.2022_IoTEnrichedEventLog,
	title        = {{An IoT-Enriched Event Log for Process Mining in Smart Factories}},
	author       = {Lukas Malburg and Joscha Grüger and Ralph Bergmann},
	year         = 2022,
	journal      = {CoRR},
	volume       = {abs/2209.02702},
	url          = {https://arxiv.org/abs/2209.02702},
	archiveprefix = {arXiv},
	eprint       = {2209.02702},
	abstract     = {Modern technologies such as the Internet of Things (IoT) are becoming increasingly important in various domains, including Business Process Management (BPM) research. One main research area in BPM is process mining, which can be used to analyze event logs, e.g., for checking the conformance of running processes. However, there are only a few IoT-based event logs available for research purposes. Some of them are artificially generated and the problem occurs that they do not always completely reflect the actual physical properties of smart environments. In this paper, we present an IoT-enriched XES event log that is generated by a physical smart factory. For this purpose, we create the SensorStream XES extension for representing IoT-data in event logs. Finally, we present some preliminary analysis and properties of the log.}
}

Modern technologies such as the Internet of Things (IoT) are becoming increasingly important in various domains, including Business Process Management (BPM) research. One main research area in BPM is process mining, which can be used to analyze event logs, e.g., for checking the conformance of running processes. However, there are only a few IoT-based event logs available for research purposes. Some of them are artificially generated and the problem occurs that they do not always completely reflect the actual physical properties of smart environments. In this paper, we present an IoT-enriched XES event log that is generated by a physical smart factory. For this purpose, we create the SensorStream XES extension for representing IoT-data in event logs. Finally, we present some preliminary analysis and properties of the log.

@article{articlegr22,
	title        = {CGK4PM: Towards a Methodology for the Systematic Generation of Clinical Guideline Process Models and the Utilization of Conformance Checking},
	author       = {Grüger, Joscha and Geyer, Tobias and Bergmann, Ralph and Braun, Stephan},
	year         = 2022,
	month        = {06},
	journal      = {BioMedInformatics},
	volume       = 2,
	pages        = {359--374},
	doi          = {10.3390/biomedinformatics2030023},
	url          = {https://www.mdpi.com/2673-7426/2/3/23/pdf?version=1656334760}
}

@article{grueger2022,
	title        = {Verifying Guideline Compliance in Clinical Treatment Using Multi-perspective Conformance Checking: A Case Study},
	author       = {Grüger, Joscha and Geyer, Tobias and Kuhn, Martin and Braun, StephanA and Bergmann, Ralph},
	year         = 2022,
	month        = {01},
	pages        = {301--313},
	doi          = {10.1007/978-3-030-98581-3_22},
	isbn         = {978-3-030-98580-6},
	url          = {https://link.springer.com/content/pdf/10.1007/978-3-030-98581-3.pdf}
}

@inproceedings{Lenz2022ComparingUnsupervisedAlgorithms,
	title = {Comparing {{Unsupervised Algorithms}} to {{Construct Argument Graphs}}},
	booktitle = {Joint {{Proceedings}} of {{Workshops}}, {{Tutorials}} and {{Doctoral Consortium}} Co-Located with the 45rd {{German Conference}} on {{Artificial Intelligence}}},
	author = {Lenz, Mirko and Dumani, Lorik and Sahitaj, Premtim},
	editor = {Koert, Dorothea and Minor, Mirjam},
	year = {2022},
	month = sep,
	series = {{{CEUR Workshop Proceedings}}},
	volume = {3457},
	publisher = {CEUR},
	address = {Virtual Event, Trier},
	issn = {1613-0073},
	url        = {http://www.wi2.uni-trier.de/shared/publications/Lenz2022ComparingUnsupervisedAlgorithms.pdf},
	abstract   = {Computational argumentation has gained considerable attention in recent years. Various areas have been addressed, such as extracting arguments from natural language texts into a structured form in order to store them in an argument base, determining stances for arguments with respect to topics, determination of inferences from statements, and much more. After so much progress has been made in the isolated tasks, in this paper we address the next level and aim to advance the automatic generation of argument graphs. To this end, we investigate various unsupervised methods for constructing the graphs and measure the performance with different metrics on three different datasets. Our implementation is publicly available on GitHub under the permissive MIT license.},
}

Computational argumentation has gained considerable attention in recent years. Various areas have been addressed, such as extracting arguments from natural language texts into a structured form in order to store them in an argument base, determining stances for arguments with respect to topics, determination of inferences from statements, and much more. After so much progress has been made in the isolated tasks, in this paper we address the next level and aim to advance the automatic generation of argument graphs. To this end, we investigate various unsupervised methods for constructing the graphs and measure the performance with different metrics on three different datasets. Our implementation is publicly available on GitHub under the permissive MIT license.

@inproceedings{Lenz2022UserCentricArgumentMining,
	title = {User-{{Centric Argument Mining}} with {{ArgueMapper}} and {{Arguebuf}}},
	booktitle = {Computational {{Models}} of {{Argument}}},
	author = {Lenz, Mirko and Bergmann, Ralph},
	year = {2022},
	series = {Frontiers in {{Artificial Intelligence}} and {{Applications}}},
	volume = {353},
	pages = {367--368},
	publisher = {IOS Press},
	address = {Cardiff, Wales},
	doi = {10.3233/FAIA220176},
	url       = {http://www.wi2.uni-trier.de/shared/publications/Lenz2022UserCentricArgumentMining.pdf},
	abstract  = {Existing tools to create argument graphs are tailored for experts in the domain of argumentation. By taking into account the needs of experts, laymen, and developers, we propose ArgueMapper as a novel argument diagramming tool and Arguebuf as its underlying format. ArgueMapper is the first of its kind to be optimized for mobile devices and provide a discoverable interface suitable for novice users. Arguebuf provides native implementations for all major programming languages via a code generation approach. To complement Arguebuf, we provide a supercharged Python implementation that enables advanced analysis. All of our contributions support AIF and are publicly available on GitHub under the MIT license.}
}

Existing tools to create argument graphs are tailored for experts in the domain of argumentation. By taking into account the needs of experts, laymen, and developers, we propose ArgueMapper as a novel argument diagramming tool and Arguebuf as its underlying format. ArgueMapper is the first of its kind to be optimized for mobile devices and provide a discoverable interface suitable for novice users. Arguebuf provides native implementations for all major programming languages via a code generation approach. To complement Arguebuf, we provide a supercharged Python implementation that enables advanced analysis. All of our contributions support AIF and are publicly available on GitHub under the MIT license.

@inproceedings{Lenz2022WorkshopTextMining,
	title = {Workshop on {{Text Mining}} and {{Generation}} ({{TMG}}): {{Preface}}},
	booktitle = {Joint {{Proceedings}} of {{Workshops}}, {{Tutorials}} and {{Doctoral Consortium}} Co-Located with the 45rd {{German Conference}} on {{Artificial Intelligence}}},
	author = {Lenz, Mirko and Dumani, Lorik and Bondarenko, Alexander and Syed, Shahbaz},
	editor = {Koert, Dorothea and Minor, Mirjam},
	year = {2022},
	month = sep,
	series = {{{CEUR Workshop Proceedings}}},
	volume = {3457},
	publisher = {CEUR},
	address = {Virtual Event, Trier},
	issn = {1613-0073},
	url        = {http://www.wi2.uni-trier.de/shared/publications/Lenz2022WorkshopTextMining.pdf},
	abstract   = {This paper is a report on the first Text Mining and Generation Workshop (TMG), which was a one-day virtual event hosted at the German Conference on Artificial Intelligence (KI 2022) in Trier, Germany. In addition to four accepted original papers, there were three invited talks by speakers who presented their works already published at high-ranked conferences as well as one keynote by a pioneer in the two research fields relevant to the workshop.},
}

This paper is a report on the first Text Mining and Generation Workshop (TMG), which was a one-day virtual event hosted at the German Conference on Artificial Intelligence (KI 2022) in Trier, Germany. In addition to four accepted original papers, there were three invited talks by speakers who presented their works already published at high-ranked conferences as well as one keynote by a pioneer in the two research fields relevant to the workshop.

@inproceedings{klein_SiameseNetworkPredM_2021,
	title        = {Using Expert Knowledge for Masking Irrelevant Data Streams in Siamese Networks for the Detection and Prediction of Faults},
	author       = {Klein, Patrick and Weingarz, Niklas and Bergmann, Ralph},
	year         = 2021,
	booktitle    = {2021 International Joint Conference on Neural Networks (IJCNN)},
	pages        = {1--10},
	doi          = {10.1109/IJCNN52387.2021.9533544},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2021_IJCNN_SiameseNetwork_PredictiveMaintenance.pdf}
}

@article{malburg_objectDetection_2021,
	title        = {{Object Detection for Smart Factory Processes by Machine Learning}},
	author       = {Lukas Malburg and Manfred-Peter Rieder and Ronny Seiger and Patrick Klein and Ralph Bergmann},
	year         = 2021,
	journal      = {Procedia Computer Science},
	booktitle    = {The 4th International Conference on Emerging Data and Industry 4.0 (EDI40), Warsaw, Poland, March 23 - 26, 2021},
	publisher    = {Elsevier.},
	volume       = 184,
	pages        = {581--588},
	doi          = {10.1016/j.procs.2021.04.009},
	url          = {https://doi.org/10.1016/j.procs.2021.04.009},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2021_MalburgEtAl_ObjectDetectionInSmartFactories.pdf},
	note         = {Best Paper.},
	keywords     = {{Process Monitoring, Object Detection, Computer Vision, Machine Learning, Industry 4.0, Cyber-Physical Production Systems}},
	abstract     = {The production industry is in a transformation towards more autonomous and intelligent manufacturing. In addition to more flexible production processes to dynamically respond to changes in the environment, it is also essential that production processes are continuously monitored and completed in time. Video-based methods such as object detection systems are still in their infancy and rarely used as basis for process monitoring. In this paper, we present a framework for video-based monitoring of manufacturing processes with the help of a physical smart factory simulation model. We evaluate three state-of-the-art object detection systems regarding their suitability to detect workpieces and to recognize failure situations that require adaptations. In our experiments, we are able to show that detection accuracies above 90% can be achieved with current object detection methods.}
}

The production industry is in a transformation towards more autonomous and intelligent manufacturing. In addition to more flexible production processes to dynamically respond to changes in the environment, it is also essential that production processes are continuously monitored and completed in time. Video-based methods such as object detection systems are still in their infancy and rarely used as basis for process monitoring. In this paper, we present a framework for video-based monitoring of manufacturing processes with the help of a physical smart factory simulation model. We evaluate three state-of-the-art object detection systems regarding their suitability to detect workpieces and to recognize failure situations that require adaptations. In our experiments, we are able to show that detection accuracies above 90% can be achieved with current object detection methods.

@article{RietzkeJODS.2021,
	title        = {{Execution of Knowledge-Intensive Processes by Utilizing Ontology-Based Reasoning}},
	author       = {Rietzke, Eric and Maletzki, Carsten and Bergmann, Ralph and Kuhn, Norbert},
	year         = 2021,
	journal      = {Journal on Data Semantics},
	publisher    = {Springer Berlin Heidelberg},
	doi          = {10.1007/s13740-021-00127-w},
	isbn         = 1374002100,
	issn         = {1861-2040},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2021_Rietzek_JODS_ODD-BP.pdf},
	abstract     = {Modeling and executing knowledge-intensive processes (KiPs) are challenging with state-of-the-art approaches, and the specific demands of KiPs are the subject of ongoing research. In this context, little attention has been paid to the ontology-driven combination of data-centric and semantic business process modeling, which finds additional motivation by enabling the division of labor between humans and artificial intelligence. Such approaches have characteristics that could allow support for KiPs based on the inferencing capabilities of reasoners. We confirm this as we show that reasoners can infer the executability of tasks based on a currently researched ontology- and data-driven business process model (ODD-BP model). Further support for KiPs by the proposed inference mechanism results from its ability to infer the relevance of tasks, depending on the extent to which their execution would contribute to process progress. Besides these contributions along with the execution perspective (start-to-end direction), we will also show how our approach can help to reach specific process goals by inferring the relevance of process elements regarding their support to achieve such goals (end-to-start direction). The elements with the most valuable process progress can be identified in the intersection of both, the execution and goal perspective. This paper will introduce this new approach and verifies its practicability with an evaluation of a KiP in the field of emergency call centers.},
	keywords     = {Data-oriented business process,Inferencing,Knowledge-intensive process,Ontology,data-oriented business process,inferencing,knowledge-intensive process,ontology}
}

Modeling and executing knowledge-intensive processes (KiPs) are challenging with state-of-the-art approaches, and the specific demands of KiPs are the subject of ongoing research. In this context, little attention has been paid to the ontology-driven combination of data-centric and semantic business process modeling, which finds additional motivation by enabling the division of labor between humans and artificial intelligence. Such approaches have characteristics that could allow support for KiPs based on the inferencing capabilities of reasoners. We confirm this as we show that reasoners can infer the executability of tasks based on a currently researched ontology- and data-driven business process model (ODD-BP model). Further support for KiPs by the proposed inference mechanism results from its ability to infer the relevance of tasks, depending on the extent to which their execution would contribute to process progress. Besides these contributions along with the execution perspective (start-to-end direction), we will also show how our approach can help to reach specific process goals by inferring the relevance of process elements regarding their support to achieve such goals (end-to-start direction). The elements with the most valuable process progress can be identified in the intersection of both, the execution and goal perspective. This paper will introduce this new approach and verifies its practicability with an evaluation of a KiP in the field of emergency call centers.

@phdthesis{RietzkeDiss.2021,
	title        = {{Modellierung, Visualisierung und Ausf{\"{u}}hrung wissensintensiver Prozesse unter Verwendung Semantischer Technologien}},
	author       = {Rietzke, Eric},
	year         = 2021,
	pages        = 203,
	url          = {http://www.wi2.uni-trier.de/shared/publications/2021_Rietzke_Dissertation.pdf},
	school       = {Universit{\"a}t Trier}
}

@inproceedings{malburg_gpuretrieval_2021,
	title        = {{Improving Similarity-Based Retrieval Efficiency by Using Graphic Processing Units in Case-Based Reasoning}},
	author       = {Malburg, Lukas and Hoffmann, Maximilian and Trumm, Simon and Bergmann, Ralph},
	year         = 2021,
	booktitle    = {Proceedings of the 34th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2021, North Miami Beach, Florida, USA},
	doi          = {10.32473/flairs.v34i1.128345},
	url          = {https://doi.org/10.32473/flairs.v34i1.128345},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2021_MalburgEtAl_ImprovingRetrievalByGPUs.pdf},
	note         = {Best Student Paper.},
	abstract     = {The accelerated growth of available data causes case bases of increasing sizes and thus lowers efficiency during the case retrieval phase in Case-Based Reasoning (CBR) systems. Even though, many complex and data-intensive tasks are solved by using Graphic Processing Units (GPUs), its application in CBR research has yet to advance past the early stage phase. In this paper, we present an approach to use CUDA-compatible GPUs for similarity assessment of structural, feature vector based cases. Our approach supports several syntactic and semantic similarity measures and is implemented in the open-source case-based reasoning framework ProCAKE. When comparing to current retrieval techniques that calculate similarities on the CPU, our GPU-based approach outperforms them by a factor of up to 37. In addition, our evaluation indicates that the performance gains increase with higher case complexity.}
}

The accelerated growth of available data causes case bases of increasing sizes and thus lowers efficiency during the case retrieval phase in Case-Based Reasoning (CBR) systems. Even though, many complex and data-intensive tasks are solved by using Graphic Processing Units (GPUs), its application in CBR research has yet to advance past the early stage phase. In this paper, we present an approach to use CUDA-compatible GPUs for similarity assessment of structural, feature vector based cases. Our approach supports several syntactic and semantic similarity measures and is implemented in the open-source case-based reasoning framework ProCAKE. When comparing to current retrieval techniques that calculate similarities on the CPU, our GPU-based approach outperforms them by a factor of up to 37. In addition, our evaluation indicates that the performance gains increase with higher case complexity.

@article{Hoffmann.2021_InformedMLCBR,
	title        = {Informed Machine Learning for Improved Similarity Assessment in Process-Oriented Case-Based Reasoning},
	author       = {Maximilian Hoffmann and Ralph Bergmann},
	year         = 2021,
	journal      = {CoRR},
	volume       = {abs/2106.15931},
	url          = {https://arxiv.org/pdf/2106.15931.pdf},
	note         = {Presented at the IJCAI-21 workshop on Deep Learning, Case-Based Reasoning, and AutoML: Present and Future Synergies, Montreal, August 21-26},
	archiveprefix = {arXiv},
	eprint       = {2106.15931}
}

@inproceedings{Theusch_2021_PHMe,
	title        = {{Fault Detection and Condition Monitoring in District Heating Using Smart Meter Data}},
	author       = {Theusch, Felix and Klein, Patrick and Bergmann, Ralph and Wilke, Wolfgang and Bock, Wolfgang and Weber, Adrian},
	year         = 2021,
	booktitle    = {Proceedings of the European Conference of the PHM Society 2021},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2021_PHMe_Fault_detection_and_condition_monitoring_in_district_heating_using_smart_meter_data_final.pdf},
	note         = {Accepted for publication.}
}

@inproceedings{Dumani2021ReCAPCorpusCorpus,
	title      = {The {{ReCAP Corpus}}: {{A Corpus}} of {{Complex Argument Graphs}} on {{German Education Politics}}},
	shorttitle = {The {{ReCAP Corpus}}},
	booktitle  = {{{IEEE Proceedings}} of the 15th {{International Conference}} on {{Semantic Computing}} ({{ICSC}})},
	author     = {Dumani, Lorik and Biertz, Manuel and Witry, Alex and Ludwig, Anna-Katharina and Lenz, Mirko and Ollinger, Stefan and Bergmann, Ralph and Schenkel, Ralf},
	year       = {2021},
	month      = jan,
	pages      = {248--255},
	issn       = {2325-6516},
	doi        = {10.1109/ICSC50631.2021.00083},
	url        = {http://www.wi2.uni-trier.de/shared/publications/Dumani2021ReCAPCorpusCorpus.pdf},
	abstract   = {The automatic extraction of arguments from natural language texts is a highly researched area and more important than ever today, as it is nearly impossible to manually capture all arguments on a controversial topic in a reasonable amount of time. For testing different algorithms such as the retrieval of the best arguments, which are still in their infancy, gold standards must exist. An argument consists of a claim or standpoint that is supported or opposed by at least one premise. The generic term for a claim or premise is Argumentative Discourse Unit (ADU). The relationships between ADUs can be specified by argument schemes and can lead to large graphs. This paper presents a corpus of 100 argument graphs with about 2,500 ADUs in German, which is unique in its size and the utilisation of argument schemes. The corpus is built from natural language texts like party press releases and parliamentary motions on education policies in the German federal states. Each high-quality text is presented by an argument graph and created by the use of a modified version of the annotation tool OVA. The final argument graphs resulted by merging two previously independently annotated graphs based on detailed discussions.}
}

The automatic extraction of arguments from natural language texts is a highly researched area and more important than ever today, as it is nearly impossible to manually capture all arguments on a controversial topic in a reasonable amount of time. For testing different algorithms such as the retrieval of the best arguments, which are still in their infancy, gold standards must exist. An argument consists of a claim or standpoint that is supported or opposed by at least one premise. The generic term for a claim or premise is Argumentative Discourse Unit (ADU). The relationships between ADUs can be specified by argument schemes and can lead to large graphs. This paper presents a corpus of 100 argument graphs with about 2,500 ADUs in German, which is unique in its size and the utilisation of argument schemes. The corpus is built from natural language texts like party press releases and parliamentary motions on education policies in the German federal states. Each high-quality text is presented by an argument graph and created by the use of a modified version of the annotation tool OVA. The final argument graphs resulted by merging two previously independently annotated graphs based on detailed discussions.

@inproceedings{schake_2020,
	title        = {{A Time-Series Similarity Measure for Case-Based Deviation Management to Support Flexible Workflow Execution}},
	author       = {Schake, Erik and Grumbach, Lisa and Bergmann, Ralph},
	year         = 2020,
	booktitle    = {Case-{Based} {Reasoning} {Research} and {Development}: 28th {International} {Conference}, {ICCBR} 2020, {Salamanca}, {Spain}, {June} 8-12, 2020, {Proceedings}},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	url          = {http://www.wi2.uni-trier.de/publications/2020_Case_Retrieval_for_Workflow_Flexibility.pdf},
	note         = {Accepted for publication. The original publication will be available at www.springerlink.com},
	editor       = {Watson, Ian and Weber, Rosina}
}

@inproceedings{hoffmann_graph_embedding_2020,
	title        = {{Using Siamese Graph Neural Networks for Similarity-Based Retrieval in Process-Oriented Case-Based Reasoning}},
	author       = {Maximilian Hoffmann and Lukas Malburg and Patrick Klein and Ralph Bergmann},
	year         = 2020,
	booktitle    = {Case-Based Reasoning Research and Development - 28th International Conference, {ICCBR} 2020, Salamanca, Spain, June 8-12, 2020, Proceedings},
	publisher    = {Springer},
	series       = {Lecture Notes in Computer Science},
	volume       = 12311,
	pages        = {229--244},
	doi          = {10.1007/978-3-030-58342-2\_15},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2020_ICCBR__Workflow_Graph_Embedding.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Ian Watson and Rosina O. Weber},
	abstract     = {Similarity-based retrieval of semantic graphs is widely used in real-world scenarios, e.g., in the domain of business workflows. To tackle the problem of complex and time-consuming graph similarity computations during retrieval, the MAC/FAC approach is used in Process- Oriented Case-Based Reasoning (POCBR), where similar graphs are extracted from a preselected set of candidate graphs. These graphs result from a similarity computation with a computationally inexpensive similarity measure. The contribution of this paper is a novel similarity measure where vector space embeddings generated by two siamese Graph Neural Networks (GNNs) are used to approximate the similarities of a precise but therefore computationally complex graph similarity measure. This includes a special scheme for encoding semantic graphs to be used in the neural networks. The evaluation examines the quality and performance of these models in preselecting retrieval candidates and in approximating the ground-truth similarities of the graph similarity measure. The results show great potential of the approach for being used in a MAC/FAC scenario, either as a preselection model or as an approximation of the graph similarity measure.}
}

Similarity-based retrieval of semantic graphs is widely used in real-world scenarios, e.g., in the domain of business workflows. To tackle the problem of complex and time-consuming graph similarity computations during retrieval, the MAC/FAC approach is used in Process- Oriented Case-Based Reasoning (POCBR), where similar graphs are extracted from a preselected set of candidate graphs. These graphs result from a similarity computation with a computationally inexpensive similarity measure. The contribution of this paper is a novel similarity measure where vector space embeddings generated by two siamese Graph Neural Networks (GNNs) are used to approximate the similarities of a precise but therefore computationally complex graph similarity measure. This includes a special scheme for encoding semantic graphs to be used in the neural networks. The evaluation examines the quality and performance of these models in preselecting retrieval candidates and in approximating the ground-truth similarities of the graph similarity measure. The results show great potential of the approach for being used in a MAC/FAC scenario, either as a preselection model or as an approximation of the graph similarity measure.

@inproceedings{zeyen_astar_2020,
	title        = {A*-Based Similarity Assessment of Semantic Graphs},
	author       = {Christian Zeyen and Ralph Bergmann},
	year         = 2020,
	booktitle    = {Case-Based Reasoning Research and Development - 28th International Conference, {ICCBR} 2020, Salamanca, Spain, June 8-12, 2020, Proceedings},
	publisher    = {Springer},
	series       = {Lecture Notes in Computer Science},
	volume       = 12311,
	pages        = {17--32},
	doi          = {10.1007/978-3-030-58342-2\_2},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2020_ZeyenBergmann_ICCBR.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Ian Watson and Rosina O. Weber}
}

@inproceedings{klein_SiameseNN4PredM_2020,
	title        = {{Enhancing Siamese Neural Networks through Expert Knowledge for Predictive Maintenance}},
	author       = {Patrick Klein and Niklas Weingarz and Ralph Bergmann},
	year         = 2020,
	year         = 2020,
	booktitle    = {{IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning}},
	publisher    = {Springer International Publishing.},
	series       = {Communications in Computer and Information Science},
	volume       = 1325,
	doi          = {10.1007/978-3-030-66770-2\_6},
	isbn         = {978-3-030-66769-6},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2020_ECML-IoTStreams_SiameseNeuralNetwork-for-PredictiveMaintenance_Preprint.pdf},
	note         = {The original publication is available at www.springerlink.com},
	%pages       = {1--16},
	keywords     = {{Siamese Neural Network, Predictive Maintenance, Expert Knowledge, 2D Convolution, Time Series Similarity}},
	abstract     = {The data provided by cyber-physical production systems (CPPSs) to monitor their condition via data-driven predictive maintenance is often high dimensional and only a few fault and failure (FaF) examples are available. These FaFs can usually be detected in a (small) localized subset of data streams, whereas the use of all data streams induces noise that could negatively affect the training and prediction performance. In addition, a CPPS often consists of multiple similar units that generate comparable data streams and show similar failure modes. However, existing approaches for learning a similarity measure generally do not consider these two aspects. For this reason, we propose two approaches for integrating expert knowledge about class or failure mode dependent attributes into siamese neural networks (SNN). Additionally, we present an attribute-wise encoding of time series based on 2D convolutions. This enables that learned knowledge in the form of filters is shared between similar data streams, which would not be possible with conventional 1D convolutions due to their spatial focus. We evaluate our approaches against state-of-the-art time series similarity measures such as dynamic time warping, NeuralWarp, as well as a feature-based representation approach. Our results show that the integration of expert knowledge is advantageous and combined with the novel SNN architecture it is possible to achieve the best performance compared to the other investigated methods..}
}

The data provided by cyber-physical production systems (CPPSs) to monitor their condition via data-driven predictive maintenance is often high dimensional and only a few fault and failure (FaF) examples are available. These FaFs can usually be detected in a (small) localized subset of data streams, whereas the use of all data streams induces noise that could negatively affect the training and prediction performance. In addition, a CPPS often consists of multiple similar units that generate comparable data streams and show similar failure modes. However, existing approaches for learning a similarity measure generally do not consider these two aspects. For this reason, we propose two approaches for integrating expert knowledge about class or failure mode dependent attributes into siamese neural networks (SNN). Additionally, we present an attribute-wise encoding of time series based on 2D convolutions. This enables that learned knowledge in the form of filters is shared between similar data streams, which would not be possible with conventional 1D convolutions due to their spatial focus. We evaluate our approaches against state-of-the-art time series similarity measures such as dynamic time warping, NeuralWarp, as well as a feature-based representation approach. Our results show that the integration of expert knowledge is advantageous and combined with the novel SNN architecture it is possible to achieve the best performance compared to the other investigated methods..

@inproceedings{malburg_SemanticWebServices_2020,
	title        = {{Semantic Web Services for AI-Research with Physical Factory Simulation Models in Industry 4.0}},
	author       = {Lukas Malburg and Patrick Klein and Ralph Bergmann},
	year         = 2020,
	booktitle    = {Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics, {IN4PL} 2020, Budapest, Hungary, November 2-4, 2020},
	publisher    = {{SCITEPRESS.}},
	pages        = {32--43},
	pages        = {32--43},
	doi          = {10.5220/0010135900320043},
	isbn         = {978-989-758-476-3},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2020_IN4PL_Semantic_Web_Services_MalburgEtAl.pdf},
	editor       = {Herv{\'{e}} Panetto and Kurosh Madani and Alexander V. Smirnov},
	keywords     = {{Semantic Web Services, Industry 4.0, Artificial Intelligence, Flexible Cyber-Physical Workflows, OWL-S, WSMO}},
	abstract     = {The transition to Industry 4.0 requires smart manufacturing systems that are easily configurable and provide a high level of flexibility during manufacturing in order to achieve mass customization or to support cloud manufacturing. To realize this, Cyber-Physical Systems (CPSs) combined with Artificial Intelligence (AI) methods find their way into manufacturing shop floors. For using AI methods in the context of Industry 4.0, semantic web services are indispensable to provide a reasonable abstraction of the underlying manufacturing capabilities. In this paper, we present semantic web services for AI-based research with physical factory simulation models in Industry 4.0. Therefore, we developed 68 semantic web services based on Web Ontology Language for Web Services (OWL-S) and Web Service Modeling Ontology (WSMO) and linked them to an already existing domain ontology for intelligent manufacturing control. Suitable for the requirements of CPS environments, our pre- and postconditions are verified in near real-time by invoking other semantic web services in contrast to complex reasoning within the knowledge base. Finally, we evaluate our implementation by executing a cyber-physical workflow composed of semantic web services using a state-of-the-art workflow management system.}
}

The transition to Industry 4.0 requires smart manufacturing systems that are easily configurable and provide a high level of flexibility during manufacturing in order to achieve mass customization or to support cloud manufacturing. To realize this, Cyber-Physical Systems (CPSs) combined with Artificial Intelligence (AI) methods find their way into manufacturing shop floors. For using AI methods in the context of Industry 4.0, semantic web services are indispensable to provide a reasonable abstraction of the underlying manufacturing capabilities. In this paper, we present semantic web services for AI-based research with physical factory simulation models in Industry 4.0. Therefore, we developed 68 semantic web services based on Web Ontology Language for Web Services (OWL-S) and Web Service Modeling Ontology (WSMO) and linked them to an already existing domain ontology for intelligent manufacturing control. Suitable for the requirements of CPS environments, our pre- and postconditions are verified in near real-time by invoking other semantic web services in contrast to complex reasoning within the knowledge base. Finally, we evaluate our implementation by executing a cyber-physical workflow composed of semantic web services using a state-of-the-art workflow management system.

@inproceedings{grueger20,
	title        = {{Process Mining for Case Acquisition in Oncology: A Systematic Literature Review}},
	author       = {Grüger, Joscha and Bergmann, Ralph and Kazik, Yavuz and Kuhn, Martin},
	year         = 2020,
	booktitle    = {Proceedings of the LWDA 2020 Workshops: KDML, FGWM, FGWI-BIA, and FGDB},
	publisher    = {CEUR-WS.org},
	pages        = {162--173},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2020_PM4HC.pdf}
}

@inproceedings{dietrich20,
	title        = {{INWEND}: {Using} {CBR} to automate legal assessment in the context of the {EU} {General} {Data} {Protection} {Regulation}},
	author       = {Dietrich, Clarissa and Schriml, Sebastian and Bergmann, Ralph and Raue, Benjamin},
	year         = 2020,
	booktitle    = {Proceedings of the LWDA 2020 Workshops: KDML, FGWM, FGWI-BIA, and FGDB},
	publisher    = {CEUR-WS.org},
	pages        = {192--201},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2020_INWEND.pdf}
}

@inproceedings{malburg_BPMResearch_2020,
	title        = {{Using Physical Factory Simulation Models for Business Process Management Research}},
	author       = {Lukas Malburg and Ronny Seiger and Ralph Bergmann and Barbara Weber},
	year         = 2020,
	booktitle    = {Business Process Management Workshops - {BPM} 2020 International Workshops, Sevilla, Spain, September 13 - 18, 2020},
	publisher    = {Springer.},
	series       = {Lecture Notes in Business Information Processing},
	volume       = 397,
	pages        = {95--107},
	doi          = {10.1007/978-3-030-66498-5\_8},
	url          = {https://doi.org/10.1007/978-3-030-66498-5\_8},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2020_MalburgEtAl_BPM.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Adela del-Río-Ortega and Henrik Leopold and Flavia M. Santoro},
	keywords     = {{Cyber-Physical Production Systems, Factory Simulation Models, Business Process Management, Industry 4.0, Digital Twins}},
	abstract     = {The production and manufacturing industries are currently transitioning towards more autonomous and intelligent production lines within the Fourth Industrial Revolution (Industry 4.0). Learning Factories as small scale physical models of real shop floors are realistic platforms to conduct research in the smart manufacturing area without depending on expensive real world production lines or completely simulated data. In this work, we propose to use learning factories for conducting research in the context of Business Process Management (BPM) and Internet of Things (IoT) as this combination promises to be mutually beneficial for both research areas. We introduce our physical Fischertechnik factory models simulating a complex production line and three exemplary use cases of combining BPM and IoT, namely the implementation of a BPM abstraction stack on top of a learning factory, the experience-based adaptation and optimization of manufacturing processes, and the stream processing-based conformance checking of IoT-enabled processes.}
}

The production and manufacturing industries are currently transitioning towards more autonomous and intelligent production lines within the Fourth Industrial Revolution (Industry 4.0). Learning Factories as small scale physical models of real shop floors are realistic platforms to conduct research in the smart manufacturing area without depending on expensive real world production lines or completely simulated data. In this work, we propose to use learning factories for conducting research in the context of Business Process Management (BPM) and Internet of Things (IoT) as this combination promises to be mutually beneficial for both research areas. We introduce our physical Fischertechnik factory models simulating a complex production line and three exemplary use cases of combining BPM and IoT, namely the implementation of a BPM abstraction stack on top of a learning factory, the experience-based adaptation and optimization of manufacturing processes, and the stream processing-based conformance checking of IoT-enabled processes.

@misc{ollinger2020stance,
	title        = {Same Side Stance Classification Task: Facilitating Argument Stance Classification by Fine-tuning a BERT Model},
	author       = {Stefan Ollinger and Lorik Dumani and Premtim Sahitaj and Ralph Bergmann and Ralf Schenkel},
	year         = 2020,
	url          = {https://arxiv.org/abs/2004.11163},
	eprint       = {2004.11163},
	archiveprefix = {arXiv},
	primaryclass = {cs.CL}
}

@article{althoff2020AIMag,
	title        = {The 27th International Conference on Case-Based Reasoning},
	author       = {Althoff, Klaus-Dieter and Bach, Kerstin and Bergmann, Ralph and Marling, Cindy},
	year         = 2020,
	journal      = {AI Magazine},
	volume       = 41,
	number       = 1,
	pages        = {101--102}
}

@article{Bergmann2020ReCAPProject,
	title    = {The {{ReCAP Project}}},
	author   = {Bergmann, Ralph and Biertz, Manuel and Dumani, Lorik and Lenz, Mirko and Ludwig, Anna-Katharina and Neumann, Patrick J. and Ollinger, Stefan and Sahitaj, Premtim and Schenkel, Ralf and Witry, Alex},
	year     = {2020},
	month    = jun,
	journal  = {Datenbank Spektrum},
	volume   = {20},
	pages    = {93--98},
	doi      = {10.1007/s13222-020-00340-0},
	url      = {http://www.wi2.uni-trier.de/shared/publications/Bergmann2020ReCAPProject.pdf},
	abstract = {Argumentation Machines search for arguments in natural language from information sources on the Web and reason with them on the knowledge level to actively support the deliberation and synthesis of arguments for a~particular user query. The recap project is part of the Priority Program ratio and aims at novel contributions to and confluence of methods from information retrieval, knowledge representation, as well as case-based reasoning for the development of future argumentation machines. In this paper we summarise recent research results from the project. In particular, a~new German corpus of 100 semantically annotated argument graphs from the domain of education politics has been created and is made available to the argumentation research community. Further, we discuss a~comprehensive investigation in finding arguments and argument graphs. We introduce a~probabilistic ranking framework for argument retrieval, i.e. for finding good premises for a~designated claim. For finding argument graphs, we developed methods for case-based argument retrieval considering the graph structure of an argument together with textual and ontology-based similarity measures applied to claims, premises, and argument schemes.}
}

Argumentation Machines search for arguments in natural language from information sources on the Web and reason with them on the knowledge level to actively support the deliberation and synthesis of arguments for a particular user query. The recap project is part of the Priority Program ratio and aims at novel contributions to and confluence of methods from information retrieval, knowledge representation, as well as case-based reasoning for the development of future argumentation machines. In this paper we summarise recent research results from the project. In particular, a new German corpus of 100 semantically annotated argument graphs from the domain of education politics has been created and is made available to the argumentation research community. Further, we discuss a comprehensive investigation in finding arguments and argument graphs. We introduce a probabilistic ranking framework for argument retrieval, i.e. for finding good premises for a designated claim. For finding argument graphs, we developed methods for case-based argument retrieval considering the graph structure of an argument together with textual and ontology-based similarity measures applied to claims, premises, and argument schemes.

@inproceedings{Lenz2020ArgumentMiningPipeline,
	title = {Towards an {{Argument Mining Pipeline Transforming Texts}} to {{Argument Graphs}}},
	booktitle = {Proceedings of the 8th {{International Conference}} on {{Computational Models}} of {{Argument}}},
	author = {Lenz, Mirko and Sahitaj, Premtim and Kallenberg, Sean and Coors, Christopher and Dumani, Lorik and Schenkel, Ralf and Bergmann, Ralph},
	editor = {Prakken, Henry and Bistarelli, Stefano and Santini, Francesco and Taticchi, Carlo},
	year = {2020},
	series = {Frontiers in {{Artificial Intelligence}} and {{Applications}}},
	volume = {326},
	eprint = {2006.04562},
	pages = {263--270},
	publisher = {IOS Press},
	address = {Perugia, Italy},
	doi = {10.3233/FAIA200510},
	url        = {http://www.wi2.uni-trier.de/shared/publications/Lenz2020ArgumentMiningPipeline.pdf},
	abstract   = {This paper tackles the automated extraction of components of argumentative information and their relations from natural language text. Moreover, we address a current lack of systems to provide a complete argumentative structure from arbitrary natural language text for general usage. We present an argument mining pipeline as a universally applicable approach for transforming German and English language texts to graph-based argument representations. We also introduce new methods for evaluating the performance based on existing benchmark argument structures. Our results show that the generated argument graphs can be beneficial to detect new connections between different statements of an argumentative text.},
}

This paper tackles the automated extraction of components of argumentative information and their relations from natural language text. Moreover, we address a current lack of systems to provide a complete argumentative structure from arbitrary natural language text for general usage. We present an argument mining pipeline as a universally applicable approach for transforming German and English language texts to graph-based argument representations. We also introduce new methods for evaluating the performance based on existing benchmark argument structures. Our results show that the generated argument graphs can be beneficial to detect new connections between different statements of an argumentative text.

@inproceedings{DBLP:conf/bpm/Maletzki19,
	title        = {{Utilizing Ontology-Based Reasoning to Support the Execution of Knowledge-Intensive Processes}},
	author       = {Maletzki, Carsten and Rietzke, Eric and Grumbach, Lisa and Bergmann, Ralph and Kuhn, Norbert},
	year         = 2019,
	booktitle    = {Business Process Management Workshops - {BPM} 2019 International Workshops},
	url          = {http://www.wi2.uni-trier.de/publications/2019-AI4BPM-Utilizing-ODD-BP.pdf}
}

@techreport{zeyen_2019_technical_report,
	title        = {On the Use of Scientific Workflows for Digital Humanities Research},
	author       = {Hess, Jan and Zeyen, Christian},
	year         = 2019,
	url          = {http://www.wi2.uni-trier.de/publications/2019_Hess_Zeyen_Technical_Report.pdf},
	institution  = {University of Trier, Department of Business Information Systems II}
}

@inproceedings{zeyen_dc_iccbr_2019,
	title        = {Interactive Assistance for Scientic Workflow Modeling by Case-Based Reasoning},
	author       = {Christian Zeyen},
	year         = 2019,
	booktitle    = {Workshops Proceedings for the Twenty-seventh International Conference on Case-Based Reasoning co-located with the Twenty-seventh International Conference on Case-Based Reasoning {(ICCBR} 2019), Otzenhausen, Germany, September 8-12, 2019},
	publisher    = {CEUR-WS.org},
	series       = {{CEUR} Workshop Proceedings},
	volume       = 2567,
	pages        = {205--209},
	url          = {http://ceur-ws.org/Vol-2567/paper23.pdf},
	editor       = {Stelios Kapetanakis and Hayley Borck}
}

@inproceedings{zeyen_scientificWF_adaptation_2019,
	title        = {{Adaptation of Scientific Workflows by Means of Process-Oriented Case-Based Reasoning}},
	author       = {Zeyen, Christian and Malburg, Lukas and Bergmann, Ralph},
	year         = 2019,
	year         = 2019,
	booktitle    = {Case-{Based} {Reasoning} {Research} and {Development}: 27th {International} {Conference}, {ICCBR} 2019, {Otzenhausen}, {Germany}, {September} 8-12, 2019, {Proceedings}},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	pages        = {388--403},
	doi          = {10.1007/978-3-030-29249-2\_26},
	url          = {https://doi.org/10.1007/978-3-030-29249-2\_26},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2019_ZeyenMalburgBergmann_ICCBR.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Kerstin Bach and Cindy Marling},
	keywords     = {{Process-Oriented Case-Based Reasoning, Workflow Adaptation, Scientific Workflows}},
	abstract     = {This paper investigates automatic adaptation of scientific workflows in process-oriented case-based reasoning with the goal of providing modeling assistance. With regard to our previous work on the adaptation of business workflows, we discuss the differences between the workflow types and the implications for transferring the approaches to scientific workflows. An experimental evaluation with RapidMiner workflows demonstrates that the approaches can significantly improve workflows towards a given query while mostly maintaining their executability and semantic correctness.},
	crossref     = {DBLP:conf/iccbr/2019}
}

This paper investigates automatic adaptation of scientific workflows in process-oriented case-based reasoning with the goal of providing modeling assistance. With regard to our previous work on the adaptation of business workflows, we discuss the differences between the workflow types and the implications for transferring the approaches to scientific workflows. An experimental evaluation with RapidMiner workflows demonstrates that the approaches can significantly improve workflows towards a given query while mostly maintaining their executability and semantic correctness.

@inproceedings{block_ArgGrapSimilarity_2019,
	title        = {{Clustering of Argument Graphs Using Semantic Similarity Measures}},
	author       = {Block, Karsten and Trumm, Simon and Sahitaj, Premtim and Ollinger, Stefan and Bergmann, Ralph},
	year         = 2019,
	booktitle    = {{KI} 2019: Advances in {Artificial Intelligence}: 42nd {German} {Conference} on {AI}, {Kassel}, {Germany}, {September} 23–26, 2019, {Proceedings}},
	publisher    = {Springer},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2019_BlockTrummSahitajOllingerBergmann_KI.pdf},
	note         = {Accepted for publication. The original publication will be available at www.springerlink.com},
	keywords     = {{Argumentation, Argument graph similarity, Semantic textual similarity, Text clustering}},
	abstract     = {Research on argumentation in Artificial Intelligence recently investigates new methods that contribute to the vision of developing robust argumentation machines. One line of research explores ways of reasoning with natural language arguments coming from information sources on the web as a foundation for the deliberation and synthesis of arguments in specific domains. This paper builds upon arguments represented as argument graphs in the standardized Argument Interchange Format. While previous work was focused on the development of semantic similarity measures used for the case-based retrieval of argument graphs, this paper addresses the problem of clustering argument graphs to explore structures that facilitate argumentation interpretation. We propose a k-medoid and an agglomerative clustering approach based on semantic similarity measures. We compare the clustering results based on a graph-based semantic measure that takes the structure of the argument into account with a semantic word2vec measure on the pure textual argument representation. Experiments based on the Microtext corpus show that the graph-based similarity is best on internal evaluation measures, while the pure textual measure performs very well for identifying topic-specific clusters.}
}

Research on argumentation in Artificial Intelligence recently investigates new methods that contribute to the vision of developing robust argumentation machines. One line of research explores ways of reasoning with natural language arguments coming from information sources on the web as a foundation for the deliberation and synthesis of arguments in specific domains. This paper builds upon arguments represented as argument graphs in the standardized Argument Interchange Format. While previous work was focused on the development of semantic similarity measures used for the case-based retrieval of argument graphs, this paper addresses the problem of clustering argument graphs to explore structures that facilitate argumentation interpretation. We propose a k-medoid and an agglomerative clustering approach based on semantic similarity measures. We compare the clustering results based on a graph-based semantic measure that takes the structure of the argument into account with a semantic word2vec measure on the pure textual argument representation. Experiments based on the Microtext corpus show that the graph-based similarity is best on internal evaluation measures, while the pure textual measure performs very well for identifying topic-specific clusters.

@inproceedings{klein_workflow_embedding_2019,
	title        = {{Learning Workflow Embeddings to Improve the Performance of Similarity-Based Retrieval for Process-Oriented Case-Based Reasoning}},
	author       = {Klein, Patrick and Malburg, Lukas and Bergmann, Ralph},
	year         = 2019,
	year         = 2019,
	booktitle    = {Case-{Based} {Reasoning} {Research} and {Development}: 27th {International} {Conference}, {ICCBR} 2019, {Otzenhausen}, {Germany}, {September} 8-12, 2019, {Proceedings}},
	publisher    = {Springer.},
	pages        = {188--203},
	doi          = {10.1007/978-3-030-29249-2\_13},
	url          = {https://doi.org/10.1007/978-3-030-29249-2\_13},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2019_KleinMalburgBergmann_ICCBR.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Kerstin Bach and Cindy Marling},
	keywords     = {{Process-Oriented Case-Based Reasoning, MAC/FAC Retrieval, Graph Embeddings}},
	abstract     = {In process-oriented case-based reasoning, similarity-based retrieval of workflow cases from large case bases is still a difficult issue due to the computationally expensive similarity assessment. The two-phase MAC/FAC (“Many are called, but few are chosen") retrieval has been proven useful to reduce the retrieval time but comes at the cost of an additional modeling effort for implementing the MAC phase. In this paper, we present a new approach to implement the MAC phase for POCBR retrieval, which makes use of the StarSpace embedding algorithm to automatically learn a vector representation for workflows, which can be used to significantly speed-up the MAC retrieval phase. In an experimental evaluation in the domain of cooking workflows, we show that the presented approach outperforms two existing MAC/FAC approaches on the same data.}
}

In process-oriented case-based reasoning, similarity-based retrieval of workflow cases from large case bases is still a difficult issue due to the computationally expensive similarity assessment. The two-phase MAC/FAC (“Many are called, but few are chosen") retrieval has been proven useful to reduce the retrieval time but comes at the cost of an additional modeling effort for implementing the MAC phase. In this paper, we present a new approach to implement the MAC phase for POCBR retrieval, which makes use of the StarSpace embedding algorithm to automatically learn a vector representation for workflows, which can be used to significantly speed-up the MAC retrieval phase. In an experimental evaluation in the domain of cooking workflows, we show that the presented approach outperforms two existing MAC/FAC approaches on the same data.

@inproceedings{bergmann_proCAKE_demo_2019,
	title        = {{ProCAKE: A Process-Oriented Case-Based Reasoning Framework}},
	author       = {Bergmann, Ralph and Grumbach, Lisa and Malburg, Lukas and Zeyen, Christian},
	year         = 2019,
	booktitle    = {{Workshops Proceedings for the Twenty-seventh International Conference on Case-Based Reasoning co-located with the Twenty-seventh International Conference on Case-Based Reasoning {(ICCBR} 2019), Otzenhausen, Germany, September 8-12, 2019}},
	publisher    = {CEUR-WS.org},
	series       = {{CEUR} Workshop Proceedings},
	volume       = 2567,
	pages        = {156--161},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2019_BergmannGrumbachMalburgZeyen_ICCBR_Demo.pdf},
	editor       = {Stelios Kapetanakis and Hayley Borck},
	keywords     = {{Knowledge Management, Process Management, Case-Based Reasoning}},
	abstract     = {This paper presents ProCAKE -- the process-oriented case-based knowledge engine of the CAKE framework, which has evolved from several research projects at the University of Trier over the years. ProCAKE constitutes a domain-independent framework that can be used to implement diverse structural or process-oriented case-based reasoning applications for integrated process and knowledge management. This paper gives an overview of the main components and demonstrates their application by examples.}
}

This paper presents ProCAKE – the process-oriented case-based knowledge engine of the CAKE framework, which has evolved from several research projects at the University of Trier over the years. ProCAKE constitutes a domain-independent framework that can be used to implement diverse structural or process-oriented case-based reasoning applications for integrated process and knowledge management. This paper gives an overview of the main components and demonstrates their application by examples.

@inproceedings{klein_ftonto_2019,
	title        = {{FTOnto: A Domain Ontology for a Fischertechnik Simulation Production Factory by Reusing Existing Ontologies}},
	author       = {Klein, Patrick and Malburg, Lukas and Bergmann, Ralph},
	year         = 2019,
	booktitle    = {Proceedings of the Conference on "Lernen, Wissen, Daten, Analysen", Berlin, Germany, September 30 - October 2, 2019.},
	publisher    = {CEUR-WS.org},
	series       = {{CEUR} Workshop Proceedings},
	volume       = 2454,
	pages        = {253--264},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2019_KleinMalburgBergmann_LWDA.pdf},
	editor       = {Robert J{\"{a}}schke and Matthias Weidlich},
	keywords     = {{Ontology Engineering, Industry 4.0, Simulation Factory, Fischertechnik}},
	abstract     = {Nowadays, semantic information provided by an ontology is indispensable in the context of Industry 4.0, especially when using methods from Artificial Intelligence. The currently available ontologies do not satisfy the demands of simulation environments used for research purposes. For this reason, we develop an ontology customized to Fischertechnik simulation factories by reusing existing ontologies. The ontology has been created according to requirements from two use cases. In our evaluation, it is determined that the ontology is suitable to represent machine components and their relationships while satisfying the specified requirements.}
}

Nowadays, semantic information provided by an ontology is indispensable in the context of Industry 4.0, especially when using methods from Artificial Intelligence. The currently available ontologies do not satisfy the demands of simulation environments used for research purposes. For this reason, we develop an ontology customized to Fischertechnik simulation factories by reusing existing ontologies. The ontology has been created according to requirements from two use cases. In our evaluation, it is determined that the ontology is suitable to represent machine components and their relationships while satisfying the specified requirements.

@inproceedings{klein_complexdatagen_2019,
	title        = {{Generation of Complex data for AI-Based Predictive Maintenance Research With a Physical Factory Model}},
	author       = {Klein, Patrick and Bergmann, Ralph},
	year         = 2019,
	booktitle    = {16th {International} {Conference} on {Informatics} in {Control} {Automation} and {Robotics}, {ICINCO} 2019, {Prague}, {Czech} {Republic}, {Proceedings}},
	publisher    = {SciTePress.},
	volume       = 1,
	pages        = {40--50},
	doi          = {10.5220/0007830700400050},
	isbn         = {978-989-758-380-3},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2019_KleinBergmann_Predictive_Maintenance_data_generation_machine_learning.pdf},
	organization = {INSTICC}
}

@inproceedings{Bergmann2019SimilarityMeasuresCaseBased,
  title = {Similarity {{Measures}} for {{Case-Based Retrieval}} of {{Natural Language Argument Graphs}} in {{Argumentation Machines}}},
  booktitle = {Proceedings of the {{Thirty-Second International Florida Artificial Intelligence Research Society Conference}}},
  author = {Bergmann, Ralph and Lenz, Mirko and Ollinger, Stefan and Pfister, Maximilian},
  editor = {Bart{\'a}k, Roman and Brawner, Keith W.},
  year = {2019},
  month = may,
  pages = {329--334},
  publisher = {AAAI Press},
  address = {Sarasota, Florida, USA},
  url = {http://www.wi2.uni-trier.de/shared/publications/Bergmann2019SimilarityMeasuresCaseBased.pdf},
  abstract = {In the field of argumentation, the vision of robust argumentation machines is investigated. They explore natural language arguments from information sources on the web and reason with them on the knowledge level to actively support the deliberation and synthesis of arguments for a particular user query. We aim at combining methods from case-based reasoning (CBR), information retrieval, and computational argumentation to contribute to the foundations of argumentation machines. In this paper, we focus on the retrieval phase of a CBR approach for an argumentation machine and propose similarity measures for arguments represented as argument graphs. We evaluate the similarity measures on a corpus of annotated micro texts and demonstrate the benefit of semantic similarity measures and the relevance of structural aspects.}
}

In the field of argumentation, the vision of robust argumentation machines is investigated. They explore natural language arguments from information sources on the web and reason with them on the knowledge level to actively support the deliberation and synthesis of arguments for a particular user query. We aim at combining methods from case-based reasoning (CBR), information retrieval, and computational argumentation to contribute to the foundations of argumentation machines. In this paper, we focus on the retrieval phase of a CBR approach for an argumentation machine and propose similarity measures for arguments represented as argument graphs. We evaluate the similarity measures on a corpus of annotated micro texts and demonstrate the benefit of semantic similarity measures and the relevance of structural aspects.

@inproceedings{Lenz2019SemanticTextualSimilarity,
  title = {Semantic {{Textual Similarity Measures}} for {{Case-Based Retrieval}} of {{Argument Graphs}}},
  booktitle = {Case-{{Based Reasoning Research}} and {{Development}}},
  author = {Lenz, Mirko and Ollinger, Stefan and Sahitaj, Premtim and Bergmann, Ralph},
  editor = {Bach, Kerstin and Marling, Cindy},
  year = {2019},
  series = {Lecture {{Notes}} in {{Computer Science}}},
  volume = {11680},
  pages = {219--234},
  publisher = {Springer International Publishing},
  address = {Otzenhausen, Germany},
  doi = {10.1007/978-3-030-29249-2_15},
  abstract = {Argumentation is an important sub-field of Artificial Intelligence, which involves computational methods for reasoning and decision making based on argumentative structures. This paper contributes to case-based reasoning with argument graphs in the standardized Argument Interchange Format by improving the similarity-based retrieval phase. We explore a large range of novel approaches for semantic textual similarity measures (both supervised and unsupervised) and use them in the context of a graph-based similarity measure for argument graphs. In addition, the use of an ontology-based semantic similarity measure for argumentation schemes is investigated. With a range of experiments we demonstrate the strengths and weaknesses of the various methods and show that our methods can improve over our previous work. Our code is publicly available on GitHub.},
  isbn = {978-3-030-29249-2},
  langid = {english},
  url = {http://www.wi2.uni-trier.de/shared/publications/Lenz2019SemanticTextualSimilarity.pdf},
}

Argumentation is an important sub-field of Artificial Intelligence, which involves computational methods for reasoning and decision making based on argumentative structures. This paper contributes to case-based reasoning with argument graphs in the standardized Argument Interchange Format by improving the similarity-based retrieval phase. We explore a large range of novel approaches for semantic textual similarity measures (both supervised and unsupervised) and use them in the context of a graph-based similarity measure for argument graphs. In addition, the use of an ontology-based semantic similarity measure for argumentation schemes is investigated. With a range of experiments we demonstrate the strengths and weaknesses of the various methods and show that our methods can improve over our previous work. Our code is publicly available on GitHub.

@inproceedings{DBLP:conf/lwa/GrumbachB19,
	title        = {{Towards Case-Based Deviation Management for Flexible Workflows}},
	author       = {Grumbach, Lisa and Bergmann, Ralph},
	year         = 2019,
	booktitle    = {Proceedings of the Conference "Lernen, Wissen, Daten, Analysen", {LWDA} 2019, Berlin, Germany, September 30 - October 2, 2019.},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2019_GrumbachBergmann_LWDA.pdf}
}

@article{Grumbach.2019,
	title        = {{SEMAFLEX: A novel approach for implementing workflow flexibility by deviation based on constraint satisfaction problem solving}},
	author       = {Grumbach, Lisa and Bergmann, Ralph},
	year         = 2019,
	journal      = {Expert Systems},
	volume       = 34,
	number       = 4,
	doi          = {10.1111/exsy.12385},
	issn         = {02664720}
}

@inproceedings{DBLP:conf/lwa/RietzkeBK19,
	title        = {{ODD-BP} - an Ontology- and Data-Driven Business Process Model},
	author       = {Eric Rietzke and Ralph Bergmann and Norbert Kuhn},
	year         = 2019,
	booktitle    = {Proceedings of the Conference on "Lernen, Wissen, Daten, Analysen", Berlin, Germany, September 30 - October 2, 2019},
	publisher    = {CEUR-WS.org},
	series       = {{CEUR} Workshop Proceedings},
	volume       = 2454,
	pages        = {310--321},
	url          = {http://ceur-ws.org/Vol-2454/paper\_43.pdf},
	editor       = {Robert J{\"{a}}schke and Matthias Weidlich}
}

@inproceedings{grumbach_semanas_2018,
	title        = {{SEMANAS} - {Semantic} {Support} for {Grant} {Application} {Processes}},
	author       = {Grumbach, Lisa and Rietzke, Eric and Schwinn, Markus and Bergmann, Ralph and Kuhn, Norbert},
	year         = 2018,
	booktitle    = {Lernen. {Wissen}. {Daten}. {Analysen}. ({LWDA} 2018)},
	series       = {{CEUR} {Workshop} {Proceedings}},
	url          = {http://www.wi2.uni-trier.de/publications/2018_GrumbachEtAl_LWDA.pdf}
}

@inproceedings{zeyen_nutrients_2018,
	title        = {{Considering Nutrients during the Generation of Recipes by Process-Oriented Case-based Reasoning}},
	author       = {Zeyen, Christian and Hoffmann, Maximilian and M{\"u}ller, Gilbert and Bergmann, Ralph},
	year         = 2018,
	booktitle    = {Case-{Based} {Reasoning} {Research} and {Development}: 26th {International} {Conference}, {ICCBR} 2018, {Stockholm}, {Sweden}, {July} 9-12, 2018, {Proceedings}},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	volume       = 11156,
	pages        = {464--479},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2018_ZeyenHoffmannMuellerBergmann_ICCBR.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Cox, Michael T. and Funk, Peter and Begum, Shahina}
}

@inproceedings{DBLP:conf/dhd/HessLZ18,
	title        = {Text Mining und Computersimulation zur Analyse autobiographischer Texte: Einfl{\"{u}}sse auf das literarische Schaffen Klaus Manns},
	author       = {Jan Hess and Daniel S. Lebherz and Christian Zeyen},
	year         = 2018,
	booktitle    = {5. Tagung des Verbands Digital Humanities im deutschsprachigen Raum, DHd 2018, K{\"{o}}ln, Germany, February 26 - March 2, 2018},
	doi          = {10.5281/zenodo.4622472},
	url          = {https://doi.org/10.5281/zenodo.4622472},
	editor       = {Georg Vogeler}
}

@inproceedings{DBLP:conf/inf-dh/LebherzZHBTBHM18,
	title        = {Towards Scientific Workflows and Computer Simulation as a Method in Digital Humanities},
	author       = {Daniel S. Lebherz and Christian Zeyen and Jan Hess and Ralph Bergmann and Ingo J. Timm and Thomas Burch and Vera Hildenbrandt and Claudine Moulin},
	year         = 2018,
	booktitle    = {GI-Workshop: Im Spannungsfeld zwischen Tool-Building und Forschung auf Augenh{\"{o}}he - Informatik und die Digital Humanities, {INF-DH} 2018, 25.9.2018, Berlin, Germany},
	publisher    = {Gesellschaft f{\"{u}}r Informatik e.V.},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2018_Lebherz_INF-DH.pdf},
	editor       = {Manuel Burghardt and Claudia M{\"{u}}ller{-}Birn}
}

@inproceedings{zeyen_interactive_short_2018,
	title        = {{Towards an Interactive Workflow Modeling Assistance by Means of Case-Based Reasoning}},
	author       = {Zeyen, Christian and Bergmann, Ralph},
	year         = 2018,
	booktitle    = {Proceedings of the Conference "Lernen, Wissen, Daten, Analysen", {LWDA} 2018},
	publisher    = {CEUR-WS.org},
	series       = {{CEUR} Workshop Proceedings},
	volume       = 2191,
	pages        = {355--360},
	url          = {http://www.wi2.uni-trier.de/publications/2018_ZeyenBergmann_LWDA.pdf}
}

@inproceedings{klein_data_generation_2018,
	title        = {{Data Generation with a Physical Model to Support Machine Learning Research for Predictive Maintenance}},
	author       = {Klein, Patrick and Bergmann, Ralph},
	year         = 2018,
	booktitle    = {Proceedings of the Conference "Lernen, Wissen, Daten, Analysen", {LWDA} 2018},
	publisher    = {CEUR-WS.org},
	volume       = 2191,
	pages        = {179--190},
	url          = {http://www.wi2.uni-trier.de/publications/2018_KleinBergmann_LWDA.pdf},
	keywords     = {{Data Generation, Machine Learning, Predictive Maintenance, Industry 4.0}},
	abstract     = {Today, manufacturing machines are continuously equippedwith various sensors, whose data enable to derive a comprehensive picture of the current state of each machine. Predictive maintenance approaches make use of  this data in order to predict the occurrence of possible failures before they actually occur, thereby significantly reducing production and service costs. The application of machine learning to sensor data streams is an essential part of data-driven predictive maintenance in order to find the patterns in  the data that are indicators of upcoming faults.  Thus, research on machine learning for  predictive maintenance is a recent and very challenging field. However, there are currently  no appropriate data sets available that can be used for thiskind of research. In this paper we therefore propose an approach for the generation of predictive maintenance data by using a physical Fischertechnik model factory equipped with several sensors. Different ways of reproducing real failures using this model are presented as well as a general procedure for data generation.}
}

Today, manufacturing machines are continuously equippedwith various sensors, whose data enable to derive a comprehensive picture of the current state of each machine. Predictive maintenance approaches make use of this data in order to predict the occurrence of possible failures before they actually occur, thereby significantly reducing production and service costs. The application of machine learning to sensor data streams is an essential part of data-driven predictive maintenance in order to find the patterns in the data that are indicators of upcoming faults. Thus, research on machine learning for predictive maintenance is a recent and very challenging field. However, there are currently no appropriate data sets available that can be used for thiskind of research. In this paper we therefore propose an approach for the generation of predictive maintenance data by using a physical Fischertechnik model factory equipped with several sensors. Different ways of reproducing real failures using this model are presented as well as a general procedure for data generation.

@inproceedings{malburg_workflow_reuse_2018,
	title        = {{Query Model and Similarity-Based Retrieval for Workflow Reuse in the Digital Humanities}},
	author       = {Malburg, Lukas and M{\"u}nster, Nicolas and Zeyen, Christian and Bergmann, Ralph},
	year         = 2018,
	booktitle    = {Proceedings of the Conference "Lernen, Wissen, Daten, Analysen", {LWDA} 2018},
	publisher    = {CEUR-WS.org},
	volume       = 2191,
	pages        = {251--262},
	url          = {http://www.wi2.uni-trier.de/publications/2018_MalburgMuensterZeyenBergmann_LWDA.pdf},
	keywords     = {{Case-Based Reasoning, Workflow Reuse, Scientific Workflows, RapidMiner, Digital Humanities}},
	abstract     = {Scientific Workflows do not seem to be broadly used today in the Digital Humanities to perform text and data analysis. Although they have become established in e-Science, modeling new workflows is usually a demanding task, especially for novice users. Case-Based Reasoning (CBR) has been applied in the past to support the development of workflows as an experience-based activity by retrieving past workflows. A query language is needed for this purpose, but current languages do not sufficiently consider different user groups and the information they can provide. To address this issue, we present a query model to support novice as well as experienced users. We  identify common expression elements from literature and integrate them in a prototypical CBR application named Reuse Assistant to support workflow reuse in the RapidMiner workflow tool. An experimental evaluation with non-expert users indicates the potential of the Reuse Assistant to facilitate workflow reuse and thus to simplify workflow development.}
}

Scientific Workflows do not seem to be broadly used today in the Digital Humanities to perform text and data analysis. Although they have become established in e-Science, modeling new workflows is usually a demanding task, especially for novice users. Case-Based Reasoning (CBR) has been applied in the past to support the development of workflows as an experience-based activity by retrieving past workflows. A query language is needed for this purpose, but current languages do not sufficiently consider different user groups and the information they can provide. To address this issue, we present a query model to support novice as well as experienced users. We identify common expression elements from literature and integrate them in a prototypical CBR application named Reuse Assistant to support workflow reuse in the RapidMiner workflow tool. An experimental evaluation with non-expert users indicates the potential of the Reuse Assistant to facilitate workflow reuse and thus to simplify workflow development.

@inproceedings{zeyen_conversational_2018,
	title        = {A Conversational Approach to Process-Oriented Case-based Reasoning},
	author       = {Christian Zeyen and Gilbert M{\"{u}}ller and Ralph Bergmann},
	year         = 2018,
	booktitle    = {Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, {IJCAI} 2018, July 13-19, 2018, Stockholm, Sweden.},
	publisher    = {ijcai.org},
	pages        = {5404--5408},
	doi          = {https://doi.org/10.24963/ijcai.2018/762},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2018_ZeyenMuellerBergmann_IJCAI.pdf},
	editor       = {J{\'{e}}r{\^{o}}me Lang}
}

@inproceedings{recap_lwda_2018,
	title        = {{ReCAP - Information Retrieval and Case-Based Reasoning for Robust Deliberation and Synthesis of Arguments in the Political Discourse}},
	author       = {Bergmann, Ralph and Schenkel, Ralf and Dumani, Lorik and Ollinger, Stefan},
	year         = 2018,
	booktitle    = {Lernen. {Wissen}. {Daten}. {Analysen}. ({LWDA} 2018)},
	series       = {{CEUR} {Workshop} {Proceedings}},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2018_BergmannSchenkelDumaniOllinger_LWDA.pdf},
	note         = {Accepted for publication}
}

@incollection{bergmann_similarity-based_2017,
	title        = {Similarity-based {Retrieval} and {Automatic} {Adaptation} of {Semantic} {Workflows}},
	author       = {Bergmann, Ralph and M{\"u}ller, Gilbert},
	year         = 2017,
	booktitle    = {Synergies {Between} {Knowledge} {Engineering} and {Software} {Engineering}},
	publisher    = {Springer},
	series       = {Advances in {Intelligent} {Systems} and {Computing}},
	number       = 626,
	pages        = {31--54},
	editor       = {Nalepa, Grzegorz J. and Baumeister, Joachim}
}

@inproceedings{muller_complexity-aware_2017,
	title        = {Complexity-{Aware} {Generation} of {Workflows} by {Process}-{Oriented} {Case}-{Based}  {Reasoning}},
	author       = {M{\"u}ller, Gilbert and Bergmann, Ralph},
	year         = 2017,
	booktitle    = {{KI} - 40th {German} {Conference} on {Artificial} {Intelligence}  {September} 25-29th, 2017: {Dortmund}, {Germany}},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Computer} {Science}},
	volume       = 2017,
	pages        = {207--221},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2017_MuellerBergmann_KI.pdf},
	note         = {The original publication is available at www.springerlink.com}
}

@inproceedings{bergmann_ever:_2017,
	title        = {{EVER}: {Extraction} and {Processing} of {Procedural} {Experience} {Knowledge} in {Workflows}},
	author       = {Bergmann, Ralph and Minor, Mirjam and M{\"u}ller, Gilbert and Schumacher, Pol},
	year         = 2017,
	booktitle    = {3rd {Workshop} on {Process} {Oriented} {Case}-{Based} {Reasoning}},
	publisher    = {CEUR Workshop Proceedings},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2017_BergmannMinorMuellerSchumacher_POCBR-ICCBR.pdf}
}

@inproceedings{bergmann_cooking_2017,
	title        = {Cooking made easy: {On} a novel approach to complexity-aware recipe generation},
	author       = {Bergmann, Ralph and M{\"u}ller, Gilbert},
	year         = 2017,
	booktitle    = {Workshop of the 2017 {Computer} {Cooking} {Contest}},
	publisher    = {CEUR Workshop Proceedings},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2017_MuellerBergmann_CCC.pdf}
}

@inproceedings{grumbach_using_2017,
	title        = {Using {Constraint} {Satisfaction} {Problem} {Solving} to {Enable} {Workflow} {Flexibility} by {Deviation}},
	author       = {Grumbach, Lisa and Bergmann, Ralph},
	year         = 2017,
	booktitle    = {Artificial {Intelligence} {XXXIV}},
	publisher    = {Springer},
	address      = {Cambridge},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	note         = {Best technical paper award}
}

@inproceedings{grumbach_workflow_2017,
	title        = {Workflow {Flexibility} by {Deviation} by means of {Constraint} {Satisfaction} {Problem} {Solving}},
	author       = {Grumbach, Lisa and Bergmann, Ralph},
	year         = 2017,
	booktitle    = {Lernen. {Wissen}. {Daten}. {Analysen}. ({LWDA} 2017)},
	series       = {{CEUR} {Workshop} {Proceedings}},
	pages        = 212,
	url          = {http://www.wi2.uni-trier.de/publications/2017_GrumbachBergmann_LWDA.pdf}
}

@inproceedings{rietzke_lwda_2017,
	title        = {Adaptive business process visualization for a data and constraint-based workflow approach},
	author       = {Rietzke, Eric and Bergmann, Ralph and Kuhn, Norbert},
	year         = 2017,
	booktitle    = {Lernen. {Wissen}. {Daten}. {Analysen}. ({LWDA} 2017)},
	series       = {{CEUR} {Workshop} {Proceedings}},
	pages        = 212,
	url          = {http://www.wi2.uni-trier.de/publications/2017_RietzkeEtAl_LWDA.pdf}
}

@inproceedings{rietzke_bpm_2017,
	title        = {Semantically-Oriented Business Process Visualization for a Data and Constraint-Based Workflow Approach},
	author       = {Rietzke, Eric and Bergmann, Ralph and Kuhn, Norbert},
	year         = 2017,
	booktitle    = {BPM 2017 International Workshops},
	series       = {Business Process Management Workshops},
	pages        = {142--150},
	url          = {http://www.wi2.uni-trier.de/publications/2017_RietzkeEtAl_BPAI.pdf}
}

@inproceedings{zeyen_conversational_2017,
	title        = {Conversational retrieval of cooking recipes},
	author       = {Zeyen, Christian and M{\"u}ller, Gilbert and Bergmann, Ralph},
	year         = 2017,
	booktitle    = {Workshop of the 2017 {Computer} {Cooking} {Contest}},
	publisher    = {CEUR Workshop Proceedings},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2017_ZeyenMuellerBergmann_CCC.pdf}
}

@inproceedings{zeyen_conversational_2017-1,
	title        = {Conversational {Process}-{Oriented} {Case}-{Based} {Reasoning}},
	author       = {Zeyen, Christian and M{\"u}ller, Gilbert and Bergmann, Ralph},
	year         = 2017,
	booktitle    = {Case-{Based} {Reasoning} {Research} and {Development}: 25th {International} {Conference}, {ICCBR} 2017, {Trondheim}, {Norway}, {June} 26-28, 2017, {Proceedings}},
	publisher    = {Springer International Publishing},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	volume       = 10339,
	pages        = {403--419},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2017_ZeyenMuellerBergmann_ICCBR.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Aha, David W. and Lieber, Jean}
}

@techreport{bergmann_ever_2016,
	title        = {{EVER} - {Extraction} and {Processing} of {Procedural} {Experience} {Knowledge} in {Workflows}},
	author       = {Bergmann, Ralph and Minor, Mirjam and M{\"u}ller, Gilbert and Schumacher, Pol},
	year         = 2016,
	pages        = {1--9},
	institution  = {Universit{\"a}t Trier, Lehrstuhl f{\"u}r Wirtschaftsinformatik II}
}

@inproceedings{grumbach_semaflex_2016,
	title        = {{SEMAFLEX} - {Semantic} {Integration} of {Flexible} {Workflow} and {Document} {Management}},
	author       = {Grumbach, Lisa and Rietzke, Eric and Schwinn, Markus and Bergmann, Ralph and Kuhn, Norbert and Krestel, Ralf and Mottin, Davide and M{\"u}ller, Emmanuel},
	year         = 2016,
	booktitle    = {Lernen. {Wissen}. {Daten}. {Analysen}. ({LWDA} 2016)},
	series       = {{CEUR} {Workshop} {Proceedings}},
	volume       = 1670,
	pages        = {43--50},
	url          = {http://www.wi2.uni-trier.de/publications/2016_GrumbachtEtAl_LWDA.pdf}
}

@inproceedings{pfister_ahnlichkeitsbasiertes_2016,
	title        = {{\"A}hnlichkeitsbasiertes {Retrieval} von {BPMN}-2.0-{Modellen}},
	author       = {Pfister, Maximilian and Fuchs, Florian and Bergmann, Ralph},
	year         = 2016,
	booktitle    = {Lernen. {Wissen}. {Daten}. {Analysen}. ({LWDA} 2016)},
	series       = {{CEUR} {Workshop} {Proceedings}},
	volume       = 1670,
	pages        = {54--61},
	url          = {http://www.wi2.uni-trier.de/publications/2016_PfisterFuchsBergmann_LWDA.pdf},
	editor       = {Krestel, Ralf and Mottin, Davide and M{\"u}ller, Emmanuel}
}

@inproceedings{bergmann_retrieving_2016,
	title        = {Retrieving {Adaptable} {Cases} in {Process}-{Oriented} {Case}-{Based} {Reasoning}},
	author       = {Bergmann, Ralph and M{\"u}ller, Gilbert and Zeyen, Christian and Manderscheid, Jens},
	year         = 2016,
	booktitle    = {Proceedings of the {Twenty}-{Ninth} {International} {Florida} {Artificial} {Intelligence} {Research} {Society} {Conference}},
	publisher    = {AAAI Press},
	address      = {Key Largo, Florida, USA},
	pages        = {419--424},
	url          = {http://www.wi2.uni-trier.de/publications/2016_Bergmann_Flairs.pdf},
	abstract     = {This paper presents a novel approach to retrieval in process-oriented case-based reasoning (POCBR) which considers the adaptability of workflows cases during the retrieval phase. A novel concept of adaptability in POCBR is proposed, which assesses the potential similarity increase of a case which can be gained by adaptation. The adaptability of a case is learned from the case base in an off-line pre-processing phase prior to the retrieval. The proposed approach is generic as it can be used in combination with different adaptation methods. An empirical evaluation in the domain of cooking workflows demonstrates the benefit of the approach.},
	editor       = {Markov, Zdravko and Russel, Ingrid}
}

This paper presents a novel approach to retrieval in process-oriented case-based reasoning (POCBR) which considers the adaptability of workflows cases during the retrieval phase. A novel concept of adaptability in POCBR is proposed, which assesses the potential similarity increase of a case which can be gained by adaptation. The adaptability of a case is learned from the case base in an off-line pre-processing phase prior to the retrieval. The proposed approach is generic as it can be used in combination with different adaptation methods. An empirical evaluation in the domain of cooking workflows demonstrates the benefit of the approach.

@inproceedings{minor_transferability_2016,
	title        = {On the {Transferability} of {Process}-oriented {Cases}},
	author       = {Minor, Mirjam and Bergmann, Ralph and M{\"u}ller, Jan-Martin and Sp{\"a}t, Alexander},
	year         = 2016,
	booktitle    = {Case-{Based} {Reasoning} {Research} and {Development} - 24nd {International} {Conference}, {ICCBR} 2016, {Atlanta}, {USA}. {Proceedings}},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	volume       = 9969,
	pages        = {281--294},
	url          = {http://www.wi2.uni-trier.de/publications/2016_MinorBergmann_ICCBR.pdf},
	note         = {The original publication is available at www.springerlink.com},
	abstract     = {The paper studies the feasibility of using transfer learning for process-oriented case-based reasoning. The work introduces a novel approach to transfer work ow cases from a loosely related source domain to a target domain. The idea is to develop a representation mapper based on work ow generalization, work ow abstraction, and structural analogy in vocabulary. The approach is illustrated by a pair of sample domains on two sub-?elds of customer relationship management, which have similar process objectives but di?erent tasks and data to ful?ll them. An experiment with expert ratings of transferred cases is conducted to test the feasibility of the approach with promising results for work ow modelling support.},
	editor       = {Goel, Ashok K. and Diaz-Agudo, Belen and Roth-Berghofer, Thomas}
}

The paper studies the feasibility of using transfer learning for process-oriented case-based reasoning. The work introduces a novel approach to transfer work ow cases from a loosely related source domain to a target domain. The idea is to develop a representation mapper based on work ow generalization, work ow abstraction, and structural analogy in vocabulary. The approach is illustrated by a pair of sample domains on two sub-?elds of customer relationship management, which have similar process objectives but di?erent tasks and data to ful?ll them. An experiment with expert ratings of transferred cases is conducted to test the feasibility of the approach with promising results for work ow modelling support.

@inproceedings{muller_case_2016,
	title        = {Case {Completion} of {Workflows} for {Process}-{Oriented} {Case}-{Based} {Reasoning}},
	author       = {M{\"u}ller, Gilbert and Bergmann, Ralph},
	year         = 2016,
	booktitle    = {Case-{Based} {Reasoning} {Research} and {Development} - 24nd {International} {Conference}, {ICCBR} 2016, {Atlanta}, {USA}. {Proceedings}},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	volume       = 9969,
	pages        = {295--310},
	url          = {http://www.wi2.uni-trier.de/publications/2016_MuellerBergmann_ICCBR.pdf},
	note         = {The original publication is available at www.springerlink.com},
	abstract     = {Cases available in real world domains are often incomplete and sometimes lack important information. Using incomplete cases in a CBR system can be harmful, as the lack of information can result in inappropriate similarity computations or incompletely generated adaptation knowledge. Case completion aims to overcome this issue by inferring missing information. This paper presents a novel approach to case completion for process-oriented case-based reasoning (POCBR). In particular, we address the completion of workflow cases by adding missing or incomplete dataflow information. Therefore, we combine automatically learned domain specific completion operators with generic domainindependent default rules. The empirical evaluation demonstrates that the presented completion approach is capable of deriving complete workflows with high quality and a high degree of completeness.},
	editor       = {Goel, A. and Diaz-Agudo, B. and Roth-Berghofer, Thomas}
}

Cases available in real world domains are often incomplete and sometimes lack important information. Using incomplete cases in a CBR system can be harmful, as the lack of information can result in inappropriate similarity computations or incompletely generated adaptation knowledge. Case completion aims to overcome this issue by inferring missing information. This paper presents a novel approach to case completion for process-oriented case-based reasoning (POCBR). In particular, we address the completion of workflow cases by adding missing or incomplete dataflow information. Therefore, we combine automatically learned domain specific completion operators with generic domainindependent default rules. The empirical evaluation demonstrates that the presented completion approach is capable of deriving complete workflows with high quality and a high degree of completeness.

@inproceedings{muller_cookingcake:_2015,
	title        = {{CookingCAKE}: {A} {Framework} for the adaptation of cooking recipes represented as workflows},
	author       = {M{\"u}ller, Gilbert and Bergmann, Ralph},
	year         = 2015,
	booktitle    = {Computer {Cooking} {Contest}, {Workshop} {Proceedings} {ICCBR} 2015},
	address      = {Springer},
	url          = {http://www.wi2.uni-trier.de/publications/2015_MuellerBergmannCCC.pdf}
}

@article{gorg_social_2015,
	title        = {Social workflows - {Vision} and potential study},
	author       = {G{\"o}rg, Sebastian and Bergmann, Ralph},
	year         = 2015,
	journal      = {Information Systems},
	series       = {Elsevier},
	volume       = 50,
	number       = {0},
	pages        = {1--19},
	doi          = {http://dx.doi.org/10.1016/j.is.2014.12.007},
	issn         = {0306-4379},
	url          = {http://www.wi2.uni-trier.de/publications/2015_GoergBergmannIS.pdf},
	note         = {Only preprint of this article available for download}
}

@book{bergmann_proceedings_2015,
	title        = {Proceedings of the {LWA} 2015 {Workshops}: {KDML}, {FGWM}, {IR}, and {FGDB}.},
	year         = 2015,
	series       = {{CEUR} {Workshop} {Proceedings}},
	volume       = 1458,
	url          = {http://ceur-ws.org/Vol-1458/},
	editor       = {Bergmann, Ralph and G{\"o}rg, Sebastian and M{\"u}ller, Gilbert}
}

@inproceedings{gessinger_flexible_2015,
	title        = {Flexible {Process}-{Aware} {Information} {Systems} for {Deficiency} {Management} in {Construction}},
	author       = {Gessinger, Sarah and Bergmann, Ralph},
	year         = 2015,
	booktitle    = {Proceedings of the {LWA} 2015 {Workshops}: {KDML}, {FGWM}, {IR}, and {FGDB}},
	address      = {Trier},
	series       = {{CEUR} {Workshop} {Proceedings}},
	volume       = 1458,
	pages        = {330--338},
	url          = {http://www.wi2.uni-trier.de/publications/2015_GessingerBergmannLWA.pdf},
	editor       = {Bergmann, Ralph and G{\"o}rg, Sebastian and M{\"u}ller, Gilbert}
}

@inproceedings{muller_poql:_2015,
	title        = {{POQL}: {A} {New} {Query} {Language} for {Process}-{Oriented} {Case}-{Based} {Reasoning}},
	author       = {M{\"u}ller, Gilbert and Bergmann, Ralph},
	year         = 2015,
	booktitle    = {Proceedings of the {LWA} 2015 {Workshops}: {KDML}, {FGWM}, {IR}, and {FGDB}},
	address      = {Trier},
	series       = {{CEUR} {Workshop} {Proceedings}},
	volume       = 1458,
	pages        = {247--255},
	url          = {http://www.wi2.uni-trier.de/publications/2015_MuellerBergmannLWA.pdf},
	editor       = {Bergmann, Ralph and G{\"o}rg, Sebastian and M{\"u}ller, Gilbert}
}

@inproceedings{muller_generalization_2015,
	title        = {Generalization of {Workflows} in {Process}-{Oriented} {Case}-{Based} {Reasoning}},
	author       = {M{\"u}ller, Gilbert and Bergmann, Ralph and Russel, Ingrid and Eberle, William},
	year         = 2015,
	booktitle    = {28th {International} {FLAIRS} {Conference}},
	publisher    = {AAAI Press},
	address      = {Hollywood (Florida), USA},
	pages        = {391--396},
	url          = {http://www.wi2.uni-trier.de/publications/2015_MuellerBergmannFLAIRS.pdf}
}

@inproceedings{muller_learning_2015,
	title        = {Learning and {Applying} {Adaptation} {Operators} in {Process}-{Oriented} {Case}-{Based} {Reasoning}},
	author       = {M{\"u}ller, Gilbert and Bergmann, Ralph},
	year         = 2015,
	booktitle    = {Case-{Based} {Reasoning} {Research} and {Development} - 23nd {International} {Conference}, {ICCBR} 2015, {Frankfurt}, {Germany}, {September} 28, 2015 - {September} 30, 2015. {Proceedings}},
	publisher    = {Spinger},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	volume       = 9343,
	pages        = {259--274},
	url          = {http://www.wi2.uni-trier.de/publications/2015_MuellerBergmannICCBR.pdf},
	editor       = {H{\"u}llermeier, Eyke and Minor, Mirjam}
}

@inproceedings{grumbach_view_2015,
	title        = {View {Specific} {Visualization} of {Proofs} for the {Analysis} of {Variant} {Development} {Structures}},
	author       = {Grumbach, Lisa},
	year         = 2015,
	booktitle    = {Transdisciplinary {Lifecycle} {Analysis} of {Systems} - {Proceedings} of the 22nd \{{ISPE}\} {Inc}. {International} {Conference} on {Concurrent} {Engineering}, {Delft}, {The} {Netherlands}, {July} 20-23, 2015},
	publisher    = {IOS Press},
	series       = {Advances in {Transdisciplinary} {Engineering}},
	pages        = {204--213},
	doi          = {10.3233/978-1-61499-544-9-204},
	isbn         = {978-1-61499-543-2},
	url          = {http://www.wi2.uni-trier.de/publications/2015_GrumbachISPE.pdf},
	urldate      = {2015-11-03},
	note         = {{\textcopyright}2015 \© The authors and IOS Press.},
	abstract     = {The importance of variant development structures has increased continuously over the past few years. Nowadays the keyword is mass customization. Manufacturers have to satisfy the personal needs of their clients to keep up with competitors. Individual wishes and increasing demands of customers require the possibility of flexible and nearly limitless adaptations of a product. The result is a diversity of variants in one product line. Issues occur during the development of the corresponding development structures that were related to the complexity of the arising product data. Not only the amount of functionality and therefore individual components is rising, but also the interrelationships among the single components are getting more complex. The number of new evolving variants once a feature is added increases in the worst case exponentially. The resulting complexity cannot be handled manually. Thus, a formal logic based approach has to be used to describe the underlying variability model of the product structure. These formal specifications provide a basis for algorithms, which analyse the structures in terms of finding all kinds of errors like inconsistencies or dead features. Such results include formal proofs, which reason about the derivation of the found errors. As the users who construct and manage the development structures typically have no expert knowledge about formal languages and proofs, the analysis output has to be represented in a role and user-specific way. The presented work concentrates on an approach to visualize the formal results in an understandable, adaptable and user-oriented fashion. Different concepts are elaborated, which cover the information needs of specific user groups to match their respective knowledge level. As feature models are used to represent variant development structures in a simple and compact manner, they are used as a basic visualization technique. Other views represent the proof, in fact a resolution graph, a proof tree and a proof step. One possibility to understand the proof is to simulate through the individual steps. Each of the features and relationships, which play a role in the current step, are highlighted in the feature model. The mapping between proof steps and features and their relationships simplifies the comprehension. Based on these concepts a prototype is implemented, whose functionality respects the common human computer interaction requirements. To conclude, the result is summarized and prospects on future increments, further concepts and possible improvements are given.}
}

The importance of variant development structures has increased continuously over the past few years. Nowadays the keyword is mass customization. Manufacturers have to satisfy the personal needs of their clients to keep up with competitors. Individual wishes and increasing demands of customers require the possibility of flexible and nearly limitless adaptations of a product. The result is a diversity of variants in one product line. Issues occur during the development of the corresponding development structures that were related to the complexity of the arising product data. Not only the amount of functionality and therefore individual components is rising, but also the interrelationships among the single components are getting more complex. The number of new evolving variants once a feature is added increases in the worst case exponentially. The resulting complexity cannot be handled manually. Thus, a formal logic based approach has to be used to describe the underlying variability model of the product structure. These formal specifications provide a basis for algorithms, which analyse the structures in terms of finding all kinds of errors like inconsistencies or dead features. Such results include formal proofs, which reason about the derivation of the found errors. As the users who construct and manage the development structures typically have no expert knowledge about formal languages and proofs, the analysis output has to be represented in a role and user-specific way. The presented work concentrates on an approach to visualize the formal results in an understandable, adaptable and user-oriented fashion. Different concepts are elaborated, which cover the information needs of specific user groups to match their respective knowledge level. As feature models are used to represent variant development structures in a simple and compact manner, they are used as a basic visualization technique. Other views represent the proof, in fact a resolution graph, a proof tree and a proof step. One possibility to understand the proof is to simulate through the individual steps. Each of the features and relationships, which play a role in the current step, are highlighted in the feature model. The mapping between proof steps and features and their relationships simplifies the comprehension. Based on these concepts a prototype is implemented, whose functionality respects the common human computer interaction requirements. To conclude, the result is summarized and prospects on future increments, further concepts and possible improvements are given.

@inproceedings{muller_compositional_2014,
	title        = {Compositional {Adaptation} of {Cooking} {Recipes} using {Workflow} {Streams}},
	author       = {M{\"u}ller, Gilbert and Bergmann, Ralph},
	year         = 2014,
	booktitle    = {Computer {Cooking} {Contest}, {Workshop} {Proceedings} {ICCBR} 2014},
	address      = {Springer},
	url          = {http://www.wi2.uni-trier.de/publications/2014_MuellerBergmannCCC.pdf},
	note         = {The original publication is available at www.springerlink.com}
}

@article{minor_case-based_2014,
	title        = {Case-based adaptation of workflows},
	author       = {Minor, Mirjam and Bergmann, Ralph and G{\"o}rg, Sebastian},
	year         = 2014,
	journal      = {Information Systems},
	series       = {Elsevier},
	volume       = 40,
	pages        = {142--152},
	doi          = {10.1016/j.is.2012.11.011},
	url          = {http://www.sciencedirect.com/science/article/pii/S0306437912001536}
}

@article{bergmann_similarity_2014,
	title        = {Similarity {Assessment} and {Efficient} {Retrieval} of {Semantic} {Workfows}},
	author       = {Bergmann, Ralph and Gil, Yolanda},
	year         = 2014,
	journal      = {Information Systems},
	series       = {Elsevier},
	volume       = 40,
	pages        = {115--127},
	doi          = {10.1016/j.is.2012.07.005},
	url          = {http://www.wi2.uni-trier.de/publications/2012_BergmannGilISJ.pdf},
	note         = {Only preprint of this article available for download}
}

@inproceedings{grumbach_konnen_2014,
	title        = {K{\"o}nnen spezielle {IT}-{Konzepte} f{\"u}r die alternde {Bev{\"o}lkerung} zur {Steigerung} der {Lebensqualit{\"a}t} beitragen? {Vorstellung} eines {Konzepts} f{\"u}r 55+},
	author       = {Grumbach, Lisa and Richert, Vitali and Kuhn, Elvira},
	year         = 2014,
	booktitle    = {Prozesse, {Technologie}, {Anwendungen}, {Systeme} und {Management}},
	publisher    = {Verlag News \& Media, Berlin},
	address      = {Regensburg},
	series       = {Angewandte {Forschung} in der {Wirtschaftsinformatik}},
	pages        = {388 S.}
}

@inproceedings{bergmann_collaborative_2014,
	title        = {The {Collaborative} {Agile} {Knowledge} {Engine} {CAKE}},
	author       = {Bergmann, Ralph and Gessinger, Sarah and G{\"o}rg, Sebastian and M{\"u}ller, Gilbert},
	year         = 2014,
	booktitle    = {Proceedings of the 18th {International} {Conference} on {Supporting} {Group} {Work}},
	publisher    = {ACM},
	address      = {New York, NY, USA},
	series       = {{GROUP} '14},
	pages        = {281--284},
	doi          = {10.1145/2660398.2663771},
	isbn         = {978-1-4503-3043-5},
	url          = {http://www.wi2.uni-trier.de/publications/2014_CAKE_ACM.pdf},
	urldate      = {2014-12-11},
	abstract     = {The Collaborative Agile Knowledge Engine (CAKE) is a prototypical generic software system for integrated process and knowledge management. CAKE integrates recent research results on agile workflows, process-oriented case-based reasoning, and web technologies into a common platform that can be configured to different application domains and needs. We describe the main concepts and the architecture of CAKE and sketch three example applications.},
	keywords     = {agile workflows, case-based reasoning, knowledge management, process management}
}

The Collaborative Agile Knowledge Engine (CAKE) is a prototypical generic software system for integrated process and knowledge management. CAKE integrates recent research results on agile workflows, process-oriented case-based reasoning, and web technologies into a common platform that can be configured to different application domains and needs. We describe the main concepts and the architecture of CAKE and sketch three example applications.

@article{bonjour_enhancing_2014,
	title        = {Enhancing experience reuse and learning},
	author       = {Bonjour, Eric and Geneste, Laurent and Bergmann, Ralph},
	year         = 2014,
	journal      = {Knowledge-Based Systems},
	volume       = 68,
	pages        = {1 -- 3},
	doi          = {http://doi.org/10.1016/j.knosys.2014.06.024},
	issn         = {0950-7051},
	url          = {http://www.sciencedirect.com/science/article/pii/S0950705114002457}
}

@techreport{bergmann_weda_2014,
	title        = {{WEDA} {\textendash} {Web}-basierte {Erstellung}, {Wiederverwendung}, {Dokumentation} und {Ablaufunterst{\"u}tzung} agiler {Workflows}. {Abschussbericht}.},
	author       = {Bergmann, Ralph and G{\"o}rg, Sebastian},
	year         = 2014,
	institution  = {Universit{\"a}t Trier, Lehrstuhl f{\"u}r Wirtschaftsinformatik II}
}

@inproceedings{dieterle_hybrid_2014,
	title        = {A {Hybrid} {CBR}-{ANN} {Approach} to the {Appraisal} of {Internet} {Domain} {Names}},
	author       = {Dieterle, Sebastian and Bergmann, Ralph},
	year         = 2014,
	booktitle    = {Case-{Based} {Reasoning} {Research} and {Development}, 22th {International} {Conference} on {Case}-{Based} {Reasoning}, {ICCBR} 2014},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	pages        = {95--109},
	url          = {http://www.wi2.uni-trier.de/publications/2014_DieterleBergmannICCBR.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Lamontagne, Luc and Plaza, Enric}
}

@inproceedings{muller_cluster-based_2014,
	title        = {A {Cluster}-{Based} {Approach} to {Improve} {Similarity}-{Based} {Retrieval} for {Process}-{Oriented} {Case}-{Based} {Reasoning}},
	author       = {M{\"u}ller, Gilbert and Bergmann, Ralph},
	year         = 2014,
	booktitle    = {20th {European} {Conference} on {Artificial} {Intelligence} ({ECAI} 2014)},
	address      = {IOS Press},
	pages        = {639--644},
	url          = {http://www.wi2.uni-trier.de/publications/2014_MuellerBergmannECAI.pdf},
	note         = {Preprint of this article available for download.},
	editor       = {Schaub, Torsten and Friedrich, Gerhard and O'Sullivan, Barry}
}

@inproceedings{muller_workflow_2014,
	title        = {Workflow {Streams}: {A} {Means} for {Compositional} {Adaptation} in {Process}-{Oriented} {CBR}},
	author       = {M{\"u}ller, Gilbert and Bergmann, Ralph},
	year         = 2014,
	booktitle    = {Case-{Based} {Reasoning} {Research} and {Development} - 22nd {International} {Conference}, {ICCBR} 2014, {Cork}, {Ireland}, {September} 29, 2014 - {October} 1, 2014. {Proceedings}},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	volume       = 8765,
	pages        = {315--329},
	url          = {http://www.wi2.uni-trier.de/publications/2014_MuellerBergmannICCBR.pdf},
	note         = {Best phd student paper award. The original publication is available at www.springerlink.com},
	editor       = {Lamontagne, Luc and Plaza, Enric}
}

@inproceedings{gorg_realtime_2013,
	title        = {Real-{Time} {Collaboration} and {Experience} {Reuse} {For} {Cloud} - {Based} {Workflow} {Management} {Systems}},
	author       = {G{\"o}rg, Sebastian and Bergmann, Ralph and Gessinger, Sarah and Minor, Mirjam},
	year         = 2013,
	booktitle    = {Workshop on {Social} {Business} {Process} {Management} ({SBM} 2013)},
	publisher    = {IEEE Computer Society Press},
	url          = {http://www.wi2.uni-trier.de/publications/2013_SBM_Workshop_GoergEtAl.pdf}
}

@inproceedings{gessinger_potentialanalyse_2013,
	title        = {Potentialanalyse des prozessorientierten {Wissensmanagement} f{\"u}r die {Baubranche}},
	author       = {Gessinger, Sarah and Bergmann, Ralph},
	year         = 2013,
	booktitle    = {{LWA} 2013 {Lernen} - {Wissen} - {Adaption}},
	publisher    = {Otto-Friedrich-Universit{\"a}t Bamberg},
	pages        = {212--219},
	url          = {http://www.wi2.uni-trier.de/publications/2013_LWA_Gessinger.pdf},
	editor       = {Sperker, Hans-Christian and Henrich, Andreas and Schmid, Ute}
}

@inproceedings{bergmann_workflow_2013,
	title        = {Workflow {Clustering} {Using} {Semantic} {Similarity} {Measures}},
	author       = {Bergmann, Ralph and M{\"u}ller, Gilbert and Wittkowsky, Daniel},
	year         = 2013,
	booktitle    = {{KI} 2013: {Advances} in {Artificial} {Intelligence}},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	volume       = 8077,
	url          = {http://www.wi2.uni-trier.de/publications/2013_BergmannMuellerWittkowskyKI.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Timm, Ingo and Thimm, Matthias}
}

@inproceedings{bergmann_mac/fac_2013,
	title        = {{MAC}/{FAC} {Retrieval} of {Semantic} {Workflows}},
	author       = {Bergmann, Ralph and Stromer, Alexander},
	year         = 2013,
	booktitle    = {Proceedings of the {Twenty}-{Sixth} {International} {Florida} {Artificial} {Intelligence} {Research} {Society} {Conference}, {St}. {Pete} {Beach}, {May} 22-25, 2013.},
	publisher    = {AAAI Press},
	pages        = {357 -- 362},
	url          = {http://www.wi2.uni-trier.de/publications/2013_FLAIRS_BergmannStromer.pdf},
	editor       = {Boonthum-Denecke, C. and Youngblood, G. Michael}
}

@inproceedings{gorg_resource_2013,
	title        = {A {Resource} {Model} for {Cloud}-{Based} {Workflow} {Management} {Systems} - {Enabling} {Access} {Control}, {Collaboration} and {Reuse}},
	author       = {G{\"o}rg, Sebastian and Bergmann, Ralph and Gessinger, Sarah and Minor, Mirjam},
	year         = 2013,
	booktitle    = {Proceedings of the 3rd {International} {Conference} on {Cloud} {Computing} and {Services} {Science}, {Aachen}, {Germany}, 2013},
	publisher    = {SciTePress 2013},
	pages        = {263--272},
	url          = {http://www.wi2.uni-trier.de/publications/2013_CLOSER-GoergEtAl.pdf}
}

@inproceedings{bergmann_ralph_scaling_2012,
	title        = {Scaling similarity-based retrieval of semantic workflows},
	author       = {{Bergmann, Ralph} and {Minor, Mirjam} and {Islam, Siblee} and {Schumacher, Pol} and {Stromer, Alexander}},
	year         = 2012,
	booktitle    = {{ICCBR}-{Workshop} on {Process}-oriented {Case}-{Based} {Reasoning}},
	address      = {Lyon},
	pages        = {15--24},
	url          = {http://www.wi2.uni-trier.de/publications/2012_ICCBR_POCBR_Bergmann_etal.pdf},
	editor       = {{Lamontagne, Luc} and {Recio-Garcia, Juan A.}}
}

@inproceedings{gorg_collecting_2012,
	title        = {Collecting, reusing and executing private workflows on social network platforms},
	author       = {G{\"o}rg, Sebastian and Bergmann, Ralph and Minor, Mirjam and Gessinger, Sarah and Islam, Siblee},
	year         = 2012,
	booktitle    = {Proceedings of the 21st {World} {Wide} {Web} {Conference}, {WWW} 2012, {Lyon}, {France}, {April} 16-20, 2012 ({Companion} {Volume})},
	publisher    = {ACM},
	pages        = {747--750},
	isbn         = {978-1-4503-1230-1},
	url          = {http://www.wi2.uni-trier.de/publications/2012_WWW_GoergEtal.pdf},
	editor       = {Mille, Alain and Gandon, Fabien L. and Misselis, Jacques and Rabinovich, Michael and Staab, Steffen}
}

@inproceedings{minor_confidence_2012,
	title        = {Confidence in {Workflow} {Adaptation}},
	author       = {Minor, Mirjam and Islam, Siblee and Schumacher, Pol},
	year         = 2012,
	booktitle    = {Case-{Based} {Reasoning}. {Research} and {Development}, 20th {International} {Conference} on {Case}-{Based} {Reasoning}, {ICCBR} 2012},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Computer} {Science}},
	url          = {http://www.wi2.uni-trier.de/publications/2012_ICCBR_Minor_springer.pdf},
	note         = {The original publication is available at www.springerlink.com},
	keywords     = {Adaptation, CBR}
}

@inproceedings{schumacher_extraction_2012,
	title        = {Extraction of {Procedural} {Knowledge} from the {Web}},
	author       = {Schumacher, Pol and Minor, Mirjam and Walter, Kirstin and Bergmann, Ralph},
	year         = 2012,
	booktitle    = {Proceedings of the 21st {World} {Wide} {Web} {Conference}, {WWW} 2012, {Lyon}, {France}, {April} 16-20, 2012 ({Companion} {Volume})},
	publisher    = {ACM},
	address      = {Lyon, France},
	url          = {http://www.wi2.uni-trier.de/publications/2012_WWW_SchumacherEtal.pdf},
	editor       = {Mille, Alain and Gandon, Fabien L. and Misselis, Jacques and Rabinovich, Michael and Staab, Steffen},
	file         = {wk2fp3-schumacher.pdf:C\:\\Users\\Zeyen\\Zotero\\storage\\HKDMQUMQ\\wk2fp3-schumacher.pdf:application/pdf}
}

@inproceedings{dieterle_case-based_2012,
	title        = {Case-{Based} {Appraisal} of {Internet} {Domains}},
	author       = {Dieterle, Sebastian and Bergmann, Ralph},
	year         = 2012,
	booktitle    = {Case-{Based} {Reasoning}. {Research} and {Development}, 20th {International} {Conference} on {Case}-{Based} {Reasoning}, {ICCBR} 2012},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	volume       = 7466,
	pages        = {47--61},
	url          = {http://www.wi2.uni-trier.de/publications/2012_ICCBR_Dieterle_Bergmann_springer.pdf},
	note         = {Collaborator: Diaz-Agudo, Belen},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Watson, Ian},
	keywords     = {CBR, Application}
}

@inproceedings{minor_acquiring_2011,
	title        = {Acquiring {Adaptation} {Cases} for {Scientific} {Workflows}},
	author       = {Minor, Mirjam and G{\"o}rg, Sebastian},
	year         = 2011,
	booktitle    = {Case-{Based} {Reasoning}. {Research} and {Development}, 19th {International} {Conference} on {Case}-{Based} {Reasoning}, {ICCBR} 2011},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Computer} {Science}},
	volume       = 6880,
	pages        = {166--180},
	url          = {http://www.wi2.uni-trier.de/publications/2011_ICCBR_MinorGoerg.pdf},
	note         = {The original publication is available at www.springerlink.com}
}

@phdthesis{schmalen_adaptives_2011,
	title        = {Adaptives {Workflow} {Management} - {Referenzmodell} und {Umsetzung}},
	author       = {Schmalen, Daniel},
	year         = 2011,
	address      = {80538 M{\"u}nchen},
	url          = {http://www.dr.hut-verlag.de/9783868539905.html},
	note         = {ISBN 9783868539905},
	school       = {Dr. Hut Verlag}
}

@inproceedings{walter_workflow_2011,
	title        = {Workflow {Extraction} from {Cooking} {Recipes}},
	author       = {Walter, Kirstin and Minor, Mirjam and Bergmann, Ralph},
	year         = 2011,
	booktitle    = {Proceedings of the {ICCBR} 2011 {Workshops}},
	pages        = {207--216},
	url          = {http://www.wi2.uni-trier.de/publications/2011_CCC_WalterEtal.pdf},
	editor       = {Diaz-Agudo, Belen and Cordier, Amelie}
}

@inproceedings{minor_adaptive_2011,
	title        = {Adaptive {Workflow} {Management} in the {Cloud} -{Towards} a {Novel} {Platform} as a {Service}},
	author       = {Minor, Mirjam and Bergmann, Ralph and G{\"o}rg, Sebastian},
	year         = 2011,
	booktitle    = {Proceedings of the {ICCBR} 2011 {Workshops}},
	pages        = {131--138},
	url          = {http://www.wi2.uni-trier.de/publications/2011_POCBR_MinorEtal.pdf}
}

@inproceedings{bergmann_retrieval_2011,
	title        = {Retrieval of {Semantic} {Workfows} with {Knowledge} {Intensive} {Similarity} {Measures}},
	author       = {Bergmann, Ralph and Gil, Yolanda and Ram, Ashwin and Wiratunga, N.},
	year         = 2011,
	booktitle    = {Case-{Based} {Reasoning}. {Research} and {Development}, 19th {International} {Conference} on {Case}-{Based} {Reasoning}, {ICCBR} 2011},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	volume       = 6880,
	pages        = {17--31},
	url          = {http://www.wi2.uni-trier.de/publications/2011_BergmannGilICCBR2011CRC.pdf},
	note         = {The original publication is available at www.springerlink.com}
}

@inproceedings{minor_reasoning_2011,
	title        = {Reasoning on {Business} {Processes} to {Support} {Change} {Reuse}},
	author       = {Minor, Mirjam and Bergmann, Ralph and G{\"o}rg, Sebastian and Walter, Kirstin},
	year         = 2011,
	booktitle    = {13th {IEEE} {Conference} on {Commerce} and {Enterprise} {Computing}, {CEC} 2011, {Luxembourg}-{Kirchberg}, {Luxembourg}, {September} 5-7, 2011},
	publisher    = {IEEE Computer Society},
	pages        = {18--25},
	isbn         = {978-1-4577-1542-6},
	url          = {http://www.wi2.uni-trier.de/publications/2011_CEC11_Minor_etal},
	editor       = {Hofreiter, Birgit and Dubois, Eric and Lin, Kwei-Jay and Setzer, Thomas and Godart, Claude and Proper, Erik and Bodenstaff, Lianne},
	file         = {submissionMinorCEC11.pdf:C\:\\Users\\Zeyen\\Zotero\\storage\\QZGFDTW8\\submissionMinorCEC11.pdf:application/pdf}
}

@inproceedings{sauer_inverse_2011,
	title        = {Inverse {Workflows} for {Supporting} {Agile} {Business} {Process} {Management}},
	author       = {Sauer, Thomas and Minor, Mirjam and Bergmann, Ralph},
	year         = 2011,
	booktitle    = {{WM} 2011: 6th {Conference} on {Professional} {Knowledge} {Management}},
	publisher    = {Gesellschaft f{\"u}r Informatik (GI)},
	series       = {{LNI}},
	volume       = 182,
	pages        = {204--213},
	url          = {http://www.wi2.uni-trier.de/publications/2011_WM_SauerEtAl.pdf},
	editor       = {Maier, Ronald}
}

@incollection{minor_agile_2010,
	title        = {Agile {Workflow} {Technology} for {Long}-{Term} {Processes} - {Enhanced} by {Case}-{Based} {Change} {Reuse}},
	author       = {Minor, Mirjam and Tartakovski, Alexander and Schmalen, Daniel and Bergmann, Ralph},
	year         = 2010,
	booktitle    = {Methodological {Advancements} in {Intelligent} {Information} {Technologies}: {Evolutionary} {Trends}},
	publisher    = {IGI Global},
	pages        = {279--298},
	url          = {http://www.igi-global.com/bookstore/titledetails.aspx?titleid=37223}
}

@phdthesis{sauer_automated_2010,
	title        = {Automated {Enactment} {Tracking} for {Dynamic} {Workflows}},
	author       = {Sauer, Thomas},
	year         = 2010,
	month        = may,
	address      = {80538 M{\"u}nchen},
	url          = {http://www.dr.hut-verlag.de/9783868534702.html},
	note         = {ISBN 978-3-86853-470-2},
	school       = {Dr. Hut Verlag}
}

@inproceedings{minor_reuse_2010,
	title        = {Reuse of {Pharmaceutical} {Experience} on {Patient}-individual {Formulations}},
	author       = {Minor, Mirjam and Raber, Michael},
	year         = 2010,
	booktitle    = {Lernen - {Wissen} - {Adaptivit{\"a}t}, {Proceedings} of the {LWA} 2010},
	address      = {Kassel},
	pages        = {261--266},
	url          = {http://www.wi2.uni-trier.de/publications/2010_LWA_MinorRaber.pdf},
	editor       = {Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}
}

@inproceedings{minor_adaptation_2010,
	title        = {Adaptation of {Cooking} {Instructions} {Following} the {Workflow} {Paradigm}},
	author       = {Minor, Mirjam and Bergmann, Ralph and G{\"o}rg, Sebastian and Walter, Kirstin},
	year         = 2010,
	booktitle    = {{ICCBR} 2010 {Workshop} {Proceedings}},
	pages        = {199--208},
	url          = {http://www.wi2.uni-trier.de/publications/2010_CCC.pdf},
	editor       = {Marling, Cindy}
}

@inproceedings{minor_towards_2010,
	title        = {Towards {Case}-{Based} {Adaptation} of {Workflows}},
	author       = {Minor, Mirjam and Bergmann, Ralph and G{\"o}rg, Sebastian and Walter, Kirstin},
	year         = 2010,
	booktitle    = {Case-{Based} {Reasoning}. {Research} and {Development}, 18th {International} {Conference} on {Case}-{Based} {Reasoning}, {ICCBR} 2010, {Alessandria},{Italy}, {July} 19-22, 2010.},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	volume       = 6176,
	pages        = {421--435},
	url          = {http://www.wi2.uni-trier.de/publications/2010_MinorEtalICCBR.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Montani, Stefania and Bichindaritz, Isabelle}
}

@article{bergmann_case-based_2009,
	title        = {Case-{Based} {Reasoning} - {Introduction} and {Recent} {Developments}},
	author       = {Bergmann, Ralph and Althoff, Klaus-Dieter and Minor, Mirjam and Reichle, Meike and Bach, Kerstin},
	year         = 2009,
	journal      = {K{\"u}nstliche Intelligenz},
	volume       = {1/2009},
	pages        = {5--11},
	url          = {http://www.wi2.uni-trier.de/publications/2009_KI_CBR.pdf}
}

@inproceedings{fuchs_cooking_2009,
	title        = {Cooking {Cake}},
	author       = {Fuchs, Christian and Gimmler, Christoph and G{\"u}nther, Simon and Holthof, Lukas and Bergmann, Ralph},
	year         = 2009,
	booktitle    = {Computer {Cooking} {Contest}, {Workshop} {Proceedings} {ICCBR} 2010},
	pages        = {259 -- 268},
	url          = {http://www.wi2.uni-trier.de/publications/2009_CookingCake.pdf},
	editor       = {Minor, Mirjam and Stahl, Armin and Leake, David}
}

@inproceedings{bergmann_improving_2009,
	title        = {Improving {KD}-{Tree} {Based} {Retrieval} for {Attribute} {Dependent} {Generalized} {Cases}},
	author       = {Bergmann, Ralph and Tartakovski, Alexander},
	year         = 2009,
	booktitle    = {Proceedings of the {Twenty}-{Second} {International} {Florida} {Artificial} {Intelligence} {Research} {Society} {Conference}, {May} 19-21, 2009, {Sanibel} {Island}, {Florida}, {USA}},
	publisher    = {AAAI Press},
	isbn         = {978-1-57735-419-2},
	url          = {http://www.wi2.uni-trier.de/publications/2009_flairs.pdf},
	editor       = {Lane, H. Chad and Guesgen, Hans W.}
}

@book{minor_assistenzsysteme_2009,
	title        = {Assistenzsysteme f{\"u}r {Agile} {Gesch{\"a}ftsprozesse}},
	author       = {Minor, Mirjam},
	year         = 2009,
	month        = nov,
	publisher    = {Kumulative Habilitation, Universit{\"a}t Trier},
	url          = {http://www.wi2.uni-trier.de/publications/2009_HabilVorwort_Minor.pdf}
}

@book{minor_computer_2009,
	title        = {Computer {Cooking} {Contest}, {Workshop} {Proceedings} {ICCBR} 2009},
	year         = 2009,
	publisher    = {University of Seattle},
	editor       = {Minor, Mirjam and Stahl, Armin and Leake, David}
}

@phdthesis{mougouie_integration_2009,
	title        = {Integration of {Similarity}-{Based} and {Deductive} {Reasoning} for {Knowledge} {Management}},
	author       = {Mougouie, Babak},
	year         = 2009,
	month        = may,
	address      = {80538 M{\"u}nchen},
	url          = {http://www.dr.hut-verlag.de/9783868530919.html},
	note         = {ISBN 9783868530919},
	school       = {Dr. Hut Verlag}
}

@phdthesis{kessler-bortoluzzi_mapping_2009,
	title        = {Mapping across {Medical} {Vocabularies}},
	author       = {Kessler-Bortoluzzi, Mariana},
	year         = 2009,
	month        = may,
	address      = {80538 M{\"u}nchen},
	url          = {http://www.dr.hut-verlag.de/9783868533668.html},
	note         = {ISBN 9783868533668},
	school       = {Dr. Hut Verlag}
}

@inproceedings{minor_fallstudie_2009,
	title        = {Fallstudie zum {Einsatz} agiler, prozessorientierter {Methoden} in der {Chipindustrie}},
	author       = {Minor, Mirjam and Schmalen, Daniel and Koldehoff, Andreas},
	year         = 2009,
	booktitle    = {Business {Services}: {Konzepte}, {Technologien}, {Anwendungen}, 9. {Internationale} {Tagung} {Wirtschaftsinformatik}, 25. - 27. {Februar} 2009, {Wien}},
	publisher    = {Oesterreichische Computer Gesellschaft},
	volume       = 1,
	pages        = {193 -- 201},
	url          = {http://www.wi2.uni-trier.de/publications/2009_WI_MinorEtal.pdf},
	editor       = {Hansen, Hans Robert and Karagiannis, Dimitris and Fill, Hans-Georg}
}

@inproceedings{minor_demonstration_2009,
	title        = {Demonstration of the {Agile} {Workflow} {Management} {System} {Cake} {II} {Based} on {Long}-{Term} {Office} {Workflows}},
	author       = {Minor, Mirjam and Schmalen, Daniel and Kempin, Stephan},
	year         = 2009,
	booktitle    = {{CEUR} proceedings of the {BPM}09 {Demonstration} {Track}, {Business} {Process} {Management} {Conference} 2009 ({BPM}{\textquoteright}09), {September} 2009, {Ulm}, {Germany}},
	volume       = 489,
	url          = {http://www.wi2.uni-trier.de/publications/2009_BPMDemo_MinorEtal.pdf},
	editor       = {Medeiros, Ana Karla Alves de and Weber, Barbara}
}

@article{althoff_case-based_2009,
	title        = {Case-{Based} {Reasoning}. {Special} {Issue} der {Zeitschrift} {K{\"u}nstliche} {Intelligenz}},
	year         = 2009,
	number       = {1/2009},
	url          = {http://www.kuenstliche-intelligenz.de/index.php?id=7777},
	editor       = {Althoff, Klaus-Dieter and Bergmann, Ralph}
}

@article{minor_agile_2008,
	title        = {Agile {Workflow} {Technology} and {Case}-{Based} {Change} {Reuse} for {Long}-{Term} {Processes}},
	author       = {Minor, Mirjam and Tartakovski, Alexander and Schmalen, Daniel and Bergmann, Ralph},
	year         = 2008,
	journal      = {International Journal of Intelligent Information Technologies},
	volume       = 4,
	number       = 1,
	pages        = {80--98},
	url          = {http://www.igi-global.com/bookstore/titledetails.aspx?titleid=34478&detailstype=contents}
}

@inproceedings{weber_agile_2008,
	title        = {Agile {Cooperative} {Process}-{Aware} {Information} {Systems} ({ProGility} 2008)},
	author       = {Weber, Barbara and Eshuis, Rik and Mendling, Jan and Minor, Mirjam},
	year         = 2008,
	booktitle    = {17th {IEEE} {International} {Workshops} on {Enabling} {Technologies}: {Infrastructures} for {Collaborative} {Enterprises}, {WETICE} 2008, {Rome}, {Italy}, {June} 23-25, 2008, {Proceedings}},
	publisher    = {IEEE Computer Society},
	pages        = {225--226},
	isbn         = {978-0-7695-3315-5}
}

@phdthesis{bendeck_wsm-p_2008,
	title        = {{WSM}-{P} {Workflow} {Semantic} {Matching} {Platform}},
	author       = {Bendeck, Fawsy},
	year         = 2008,
	publisher    = {Verlag Dr. Hut, 80538 M{\"u}nchen , ISBN 978-3-89963-854-7},
	url          = {http://www.dr.hut-verlag.de/9783899638547.html}
}

@inproceedings{minor_xml-based_2008,
	title        = {{XML}-based {Representation} of {Agile} {Workflows}},
	author       = {Minor, Mirjam and Schmalen, Daniel and Bergmann, Ralph},
	year         = 2008,
	booktitle    = {Multikonferenz {Wirtschaftsinformatik} 2008},
	publisher    = {GITO-Verlag Berlin},
	pages        = {439--440},
	url          = {http://www.wi2.uni-trier.de/publications/2008_MKWI_MinorEtal.pdf},
	editor       = {Bichler, Martin and Hess, Thomas and Krcmar, Helmut and Lechner, Ulrike and Matthes, Florian and Picot, Arnold and Speitkamp, Benjamin and Wolf, Petra}
}

@book{stahl_computer_2008,
	title        = {Computer {Cooking} {Contest}, {ECCBR} 2008 {Workshop} {Proceedings}},
	year         = 2008,
	publisher    = {Tharax Verlag},
	editor       = {Stahl, Armin and Minor, Mirjam and Traph{\"o}ner, Ralph}
}

@inproceedings{minor_results_2008,
	title        = {Results of a case study on process-oriented knowledge management},
	author       = {Minor, Mirjam and Schmalen, Daniel and Koldehoff, Andreas},
	year         = 2008,
	booktitle    = {{LWA} 2008 - {Workshop}-{Woche}: {Lernen}, {Wissen} \& {Adaptivit{\"a}t}, {W{\"u}rzburg}, 6.-8. {Oktober} 2008, {Proceedings}},
	publisher    = {Department of Computer Science, University of W{\"u}rzburg, Germany},
	series       = {Technical {Report}},
	volume       = 448,
	pages        = {29--32},
	url          = {http://www.wi2.uni-trier.de/publications/2008_LWA_MinorEtal.pdf},
	editor       = {Baumeister, Joachim and Atzm{\"u}ller, Martin}
}

@inproceedings{minor_introspection_2008,
	title        = {Introspection into an {Agile} {Workflow} {Engine} for {Long}-{Term} {Processes}},
	author       = {Minor, Mirjam and Schmalen, Daniel and Weidlich, Jakob and Koldehoff, Andreas},
	year         = 2008,
	booktitle    = {17th {IEEE} {International} {Workshops} on {Enabling} {Technologies}: {Infrastructures} for {Collaborative} {Enterprises}, {WETICE} 2008, {Rome}, {Italy}, {June} 23-25, 2008, {Proceedings}},
	publisher    = {IEEE Computer Society},
	pages        = {241--246},
	isbn         = {978-0-7695-3315-5},
	url          = {http://www.wi2.uni-trier.de/publications/2008_WETICE_MinorEtal.pdf}
}

@inproceedings{sauer_agent-oriented_2008,
	title        = {An {Agent}-{Oriented} {System} for {Workflow} {Enactment} {Tracking}},
	author       = {Sauer, Thomas and Minor, Mirjam and Werno, Sascha},
	year         = 2008,
	booktitle    = {Proceedings of the 2008 {IEEE} 17th {Workshop} on {Enabling} {Technologies}: {Infrastructure} for {Collaborative} {Enterprises}},
	publisher    = {IEEE Computer Society},
	address      = {Washington, DC, USA},
	pages        = {235--240},
	doi          = {10.1109/WETICE.2008.28},
	isbn         = {978-0-7695-3315-5},
	url          = {http://www.wi2.uni-trier.de/publications/2008_WETICE08.pdf},
	keywords     = {Workflow Management, Workflow Enactment Tracking, Multi-Agent Systems}
}

@book{bergmann_multiagent_2008,
	title        = {Multiagent {System} {Technologies}},
	year         = 2008,
	month        = sep,
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	volume       = 5244,
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Bergmann, Ralph and Lindemann, Gabriela and Kirn, Stefan and Pechoucek, Michal}
}

@book{althoff_advances_2008,
	title        = {Advances in {Case}-{Based} {Reasoning}, 9th {European} {Conference}, {ECCBR} 2008, {Trier}, {Germany}, {September} 2008, {Proceedings}},
	year         = 2008,
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	volume       = 5239,
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Althoff, Klaus-Dieter and Bergmann, Ralph and Minor, Mirjam and Hanft, Alexandre}
}

@techreport{maximini_collaborative_2007,
	title        = {Collaborative {Agent}-{Based} {Knowledge} {Engine}, {Anforderungen} - {Konzept} - {L{\"o}sungen}},
	author       = {Maximini, Kerstin and Maximini, Rainer},
	year         = 2007,
	month        = jan,
	address      = {Universit{\"a}t Trier, Lehstuhl f{\"u}r Wirtschaftsinformatik II, D-54286 Trier},
	number       = {V2},
	url          = {http://www.wi2.uni-trier.de/publications/2007_CAKE_Maximini-V2.pdf},
	institution  = {Universit{\"a}t Trier, Lehrstuhl f{\"u}r Wirtschaftsinformatik II}
}

@inproceedings{fresmann_mobile_2007,
	title        = {Mobile {Knowledge} {Management} {Support} in {Fire} {Service} {Organisations}},
	author       = {Fre{\ss}mann, Andrea and Bergmann, Ralph and Taylor, Brian and Diamond, Ben and Carr-Smith, Gary},
	year         = 2007,
	booktitle    = {Tagungsband der 8. {Internationalen} {Tagung} der {Wirtschaftsinformatik}},
	publisher    = {Universit{\"a}tsverlag Karlsruhe},
	volume       = 2,
	pages        = {557--574},
	url          = {http://www.wi2.uni-trier.de/publications/2007_WI_amira.pdf},
	editor       = {Oberweis and Weinhardt and Gimpel and Koschmider and Pankratius and Schnizler}
}

@inproceedings{sauer_supporting_2007,
	title        = {Supporting {Workflow} {Management} by {Automated} {Enactment} {Tracking}},
	author       = {Sauer, Thomas and Maximini, Kerstin},
	year         = 2007,
	month        = mar,
	booktitle    = {Proceedings of the 4th {German} {Workshop} on {Experience} {Management} ({GWEM}2007)},
	url          = {http://www.wi2.uni-trier.de/publications/2007_GWEM_sauer.pdf}
}

@book{bergmann_workshop_2007,
	title        = {Workshop {Proceedings} {Artificial} {Intelligence} {Methods} for {Ambient} {Intelligence}},
	year         = 2007,
	address      = {Darmstadt},
	editor       = {Bergmann, Ralph and Althoff, Klaus-Dieter and Furbach, Ulrich and Schmid, Klaus}
}

@article{minor_erfahrungsmanagement_2007,
	title        = {Erfahrungsmanagement im {\"U}berblick},
	author       = {Minor, Mirjam},
	year         = 2007,
	journal      = {K{\"u}nstliche Intelligenz},
	volume       = {4/2007},
	pages        = {5--8},
	url          = {http://www.wi2.uni-trier.de/publications/2007_KI_EM_Minor.pdf}
}

@inproceedings{bergmann_ambient_2007,
	title        = {Ambient {Intelligence} for {Decision} {Making} in {Fire} {Service} {Organizations}},
	author       = {Bergmann, Ralph},
	year         = 2007,
	booktitle    = {Ambient {Intelligence}, {European} {Conference}, {AmI} 2007, {Darmstadt}, {Germany}, {November} 7-10, 2007, {Proceedings}},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Computer} {Science}},
	volume       = 4794,
	pages        = {73--90},
	isbn         = {978-3-540-76651-3},
	url          = {http://www.wi2.uni-trier.de/publications/2007_AMI.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Schiele, Bernt and Dey, Anind K. and Gellersen, Hans and Ruyter, Boris E. R. de and Tscheligi, Manfred and Wichert, Reiner and Aarts, Emile H. L. and Buchmann, Alejandro P.}
}

@phdthesis{maximini_integriertes_2007,
	title        = {Integriertes {Wissensmanagement} in {Klinischen} {Umgebungen} - {Analyse} von {Behandlungsverl{\"a}ufen} in der {Schlaganfallversorgung}},
	author       = {Maximini, Kerstin},
	year         = 2007,
	address      = {80538 M{\"u}nchen},
	url          = {http://www.dr.hut-verlag.de/9783899635737.html},
	school       = {Verlag Dr. Hut}
}

@phdthesis{fresmann_knowledge_2007,
	title        = {Knowledge {Management} {Support} for {Collaborative} and {Mobile} {Working}},
	author       = {Fre{\ss}mann, Andrea},
	year         = 2007,
	address      = {80538 M{\"u}nchen},
	url          = {http://www.dr.hut-verlag.de/9783899635058.html},
	note         = {ISBN 3-89963-505-8},
	school       = {Dr. Hut Verlag}
}

@inproceedings{minor_role_2007,
	title        = {The {Role} of {Context} {Models} in {Association} with {Flexible} {Design} {Processes}},
	author       = {Minor, Mirjam and Schmalen, Daniel and Koldehoff, Andreas},
	year         = 2007,
	booktitle    = {Workshop-{Proceedings} {ICCBR}'07},
	pages        = {185 -- 190},
	url          = {http://www.wi2.uni-trier.de/publications/2007_CaCOa_MinorEtal.pdf},
	editor       = {Wilson, David C and Khemani, Deepak}
}

@inproceedings{minor_structural_2007,
	title        = {Structural adaptation of workflows supported by a suspension mechanism and by case-based reasoning},
	author       = {Minor, Mirjam and Schmalen, Daniel and Koldehoff, Andreas and Bergmann, Ralph},
	year         = 2007,
	booktitle    = {Proceedings of the 16th {IEEE} {International} {Workshop} on {Enabling} {Technologies}: {Infrastructure} for {Collaborative} {Enterprises} ({WETICE}'07), {June} 18 - 20, 2007, {Paris}, {France}},
	publisher    = {IEEE Computer Society, Los Alamitos, California},
	pages        = {370--375},
	url          = {http://www.wi2.uni-trier.de/publications/2007_WETICE_MinorEtal.pdf},
	note         = {Best Paper},
	editor       = {Reddy, Sumitra Mitra}
}

@inproceedings{giese_using_2007,
	title        = {Using {Ontology}-{Mapping} {Techniques} for {Content}-based {Result} {Merging}},
	author       = {Giese, Michael and Fre{\ss}mann, Andrea and Bergmann, Ralph},
	year         = 2007,
	booktitle    = {4th {Conference} on {Professional} {Knowledge} {Management} - {Experience} and {Visions}},
	publisher    = {GITO-Verlag Berlin},
	volume       = 2,
	pages        = {127--134},
	url          = {http://www.wi2.uni-trier.de/publications/2007_wm07_EM.pdf},
	editor       = {Gronau, Norbert}
}

@inproceedings{minor_configurable_2007,
	title        = {Configurable {Contexts} for {Experience} {Management}},
	author       = {Minor, Mirjam and Koldehoff, Andreas and Schmalen, Daniel and Bergmann, Ralph},
	year         = 2007,
	month        = mar,
	booktitle    = {4th {Conference} on {Professional} {Knowledge} {Management} - {Experiences} and {Visions}},
	publisher    = {GITO-Verlag Berlin},
	volume       = 2,
	pages        = {119--126},
	url          = {http://www.wi2.uni-trier.de/publications/2007_WM_MinorEtal.pdf},
	editor       = {Gronau, Norbert}
}

@inproceedings{minor_representation_2007,
	title        = {Representation and {Structure}-based {Similarity} {Assessment} for {Agile} {Workflows}},
	author       = {Minor, Mirjam and Tartakovski, Alexander and Bergmann, Ralph},
	year         = 2007,
	booktitle    = {Case-{Based} {Reasoning} {Research} and {Development}, 7th {International} {Conference} on {Case}-{Based} {Reasoning}, {ICCBR} 2007, {Belfast}, {Northern} {Ireland}, {UK}, {August} 2007, {Proceedings}},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	volume       = 4626,
	pages        = {224--238},
	url          = {http://www.wi2.uni-trier.de/publications/2007_ICCBR_MinorEtal.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Weber, Rosina O and Richter, Michael M}
}

@article{nick_experience_2007,
	title        = {Experience {Management}},
	author       = {Nick, Markus and Althoff, Klaus-Dieter and Bergmann, Ralph},
	year         = 2007,
	journal      = {KI - K{\"u}nstliche Intelligenz},
	volume       = 21,
	number       = 2,
	pages        = {50--52},
	url          = {http://www.wi2.uni-trier.de/publications/2007_Experience Management Schlagwort.pdf}
}

@inproceedings{fresmann_adaptive_2006,
	title        = {Adaptive {Workflow} {Support} for {Search} {Processes} within {Fire} {Service} {Organisations}},
	author       = {Fre{\ss}mann, Andrea},
	year         = 2006,
	month        = jun,
	booktitle    = {Proceedings of the fifteenth {IEEE} {International} {Workshop} on {Enabling} {Technologies}: {Infrastructure} for {Collaborative} {Enterprises}},
	publisher    = {IEEE Computer Society},
	pages        = {291--296},
	editor       = {Reddy, Sumitra Mitra}
}

@inproceedings{minor_flexible_2006,
	title        = {Flexible {Workflows} for {Knowledge} {Management} in the {Digital} {Design}},
	author       = {Minor, Mirjam and Koldehoff, Andreas and Schmalen, Daniel and Bergmann, Ralph},
	year         = 2006,
	booktitle    = {{LWA} 2006 {Lernen} - {Wissensentdeckung} - {Adaptivit{\"a}t}, 9.-11.10.2006 in {Hildesheim}},
	pages        = {215 -- 219},
	url          = {http://www.wi2.uni-trier.de/publications/2006_FGWM_MinorEtal.pdf},
	editor       = {Schaaf, Martin and Althoff, Klaus-Dieter}
}

@inproceedings{muller_knowledge_2006,
	title        = {Knowledge {Search} within a {Company}-{WIKI}},
	author       = {M{\"u}ller, Stephanie and Kritzler, Nils and Tartakovski, Alexander and Bergmann, Ralph and Traph{\"o}ner, Ralph},
	year         = 2006,
	booktitle    = {{LWA} 2006 : {Lernen} - {Wissensentdeckung} - {Adaptivit{\"a}t}},
	address      = {Hildesheim},
	pages        = {209--214},
	url          = {http://www.wi2.uni-trier.de/publications/2006_LWA_paper_final.pdf},
	editor       = {Schaaf, Martin and Althoff, Klaus-Dieter}
}

@inproceedings{sauer_supporting_2006,
	title        = {Supporting {Collaborative} {Business} through {Integration} of {Knowledge} {Distribution} and {Agile} {Process} {Management}},
	author       = {Sauer, Thomas and Maximini, Kerstin and Maximini, Rainer and Bergmann, Ralph},
	year         = 2006,
	month        = feb,
	booktitle    = {Multikonferenz {Wirtschaftsinformatik} 2006 ({MKWI} 2006), {Teilkonferenz} "{Collaborative} {Business}"},
	publisher    = {GITO-Verlag Berlin},
	address      = {Passau, Germany},
	pages        = {349--361},
	url          = {http://www.wi2.uni-trier.de/publications/2006_MKWI_sauer.pdf},
	editor       = {Lehner, Franz and N{\"o}sekabel, Holger and Kleinschmidt, Peter}
}

@inproceedings{sauer_using_2006,
	title        = {Using {Workflow} {Context} for {Automated} {Enactment} {State} {Tracking}},
	author       = {Sauer, Thomas and Maximini, Kerstin},
	year         = 2006,
	month        = sep,
	booktitle    = {Workshop {Proceedings}: 8th {European} {Conference} on {Case}-{Based} {Reasoning}, {\"O}l{\"u}deniz/{Fethiye}, {Turkey}, {Workshop}: "{Case}-based {Reasoning} and {Context} {Awareness} ({CACOA} 2006)"},
	publisher    = {Universit{\"a}t Trier, Department of Business Information Systems II},
	pages        = {300--314},
	url          = {http://www.wi2.uni-trier.de/publications/2006_CACOA_sauer.pdf},
	editor       = {Minor, Mirjam}
}

@techreport{thimm_workflow_2006,
	title        = {Workflow {Management} {Concepts} {For} {Agile} {Long}-{Term} {Workflows}},
	author       = {Thimm, Rouven},
	year         = 2006,
	month        = sep,
	institution  = {Diplomarbeit, Universit{\"a}t Trier}
}

@phdthesis{minor_erfahrungsmanagement_2006,
	title        = {Erfahrungsmanagement mit fallbasierten {Assistenzsystemen}},
	author       = {Minor, Mirjam},
	year         = 2006,
	month        = may,
	url          = {http://edoc.hu-berlin.de/dissertationen/minor-mirjam-2006-05-18/PDF/minor.pdf},
	school       = {Humboldt-Universit{\"a}t zu Berlin}
}

@phdthesis{maximini_advanced_2006,
	title        = {Advanced {Techniques} for {Complex} {Case}-{Based} {Reasoning} {Applications}},
	author       = {Maximini, Rainer},
	year         = 2006,
	month        = may,
	address      = {80538 M{\"u}nchen},
	url          = {http://www.dr.hut-verlag.de/389963344X.html},
	note         = {ISBN 3-89963-344-X},
	school       = {Dr. Hut Verlag}
}

@inproceedings{krechel_lenus_2006,
	title        = {{LENUS} - {The} {Hospital} {Content} {Management} {System}},
	author       = {Krechel, Dirk and Hartbauer, Markus and Maximini, Kerstin},
	year         = 2006,
	month        = jun,
	booktitle    = {Proceedings of the 19th {IEEE} {Symposium} on {Computer}-based {Medical} {Systems} ({CBMS}'06)}
}

@inproceedings{minor_automatic_2006,
	title        = {Automatic {Transformation} and {Enlargement} of {Similarity} {Models} for {Case}-{Based} {Reasoning}},
	author       = {Minor, Mirjam and Schmidt, Karsten},
	year         = 2006,
	booktitle    = {Proc. {Modellierung} 2006, 22. - 24. {M{\"a}rz} 2006, {Innsbruck}, {Tirol}, {Austria}},
	publisher    = {GI-Edition},
	address      = {Bonn},
	series       = {{LNI} {P}-82},
	pages        = {293 -- 296},
	url          = {http://www.wi2.uni-trier.de/publications/2006_Modellierung_MinorSchmidt.pdf},
	editor       = {Mayr, Heinrich C and Breu, Ruth}
}

@inproceedings{minor_flexible_2006-1,
	title        = {Flexible {Workflows} for {Digital} {Design} in the {Nano} {Era}},
	author       = {Minor, Mirjam and Koldehoff, Andreas and Schmalen, Daniel and Bergmann, Ralph},
	year         = 2006,
	booktitle    = {Proceedings of the {Fifteenth} {IEEE} {International} {Workshop} on {Enabling} {Technologies}: {Infrastructure} for {Collaborative} {Enterprises} ({WETICE} 2006), {Workshop} {ProFlex} 2006, {June} 26 - 28, {Manchester}, {UK}},
	publisher    = {IEEE Computer Society, Los Alamitos, California},
	pages        = {273 -- 278},
	url          = {http://www.wi2.uni-trier.de/publications/2006_WETICE_MinorEtal.pdf},
	editor       = {Weber, Barbara and Mendling, Jan}
}

@inproceedings{minor_experience_2006,
	title        = {Experience {Management} with {Case}-{Based} {Assistant} {Systems}},
	author       = {Minor, Mirjam},
	year         = 2006,
	booktitle    = {Advances in {Case}-{Based} {Reasoning}, 8th {European} {Conference}, {ECCBR} 2006, {Fethiye}, {Turkey}, {September} 2006, {Proceedings}},
	publisher    = {Springer-Verlag},
	series       = {{LNAI} 4106},
	pages        = {182--195},
	url          = {http://www.wi2.uni-trier.de/publications/2006_ECCBR_Minor.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Roth-Berghofer, Thomas R and G{\"o}ker, Mehmet H and G{\"u}venir, H. Altay}
}

@inproceedings{bergmann_case-based_2006,
	title        = {Case-{Based} {Support} for {Collaborative} {Business}},
	author       = {Bergmann, Ralph and Fre{\ss}mann, Andrea and Maximini, Kerstin and Maximini, Rainer and Sauer, Thomas},
	year         = 2006,
	booktitle    = {Advances in {Case}-{Based} {Reasoning}, 8th {European} {Conference}, {ECCBR} 2006, {Fethiye}, {Turkey}, {September} 4-7, 2006, {Proceedings}},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	volume       = 4106,
	pages        = {519--533},
	isbn         = {3-540-36843-4},
	url          = {http://www.wi2.uni-trier.de/publications/2006_ECCBR_bergmann.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Roth-Berghofer, Thomas and G{\"o}ker, Mehmet H. and G{\"u}venir, H. Altay}
}

@inproceedings{bergmann_finding_2006,
	title        = {Finding {Similar} {Deductive} {Consequences} - {A} {New} {Search}-{Based} {Framework} for {Unified} {Reasoning} from {Cases} and {General} {Knowledge}},
	author       = {Bergmann, Ralph and Mougouie, Babak},
	year         = 2006,
	booktitle    = {Advances in {Case}-{Based} {Reasoning} - {Proceedings} of the 8th {European} {Conference}, {ECCBR} 2006, {Fethiye}, {Turkey}},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	volume       = 4106,
	pages        = {271--285},
	isbn         = {3-540-36843-4},
	url          = {http://www.wi2.uni-trier.de/publications/2006_eccbr_bergmann_mougouie_final.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Roth-Berghofer, Thomas and G{\"o}ker, Mehmet and G{\"u}venir, H. Altay}
}

@inproceedings{minor_learning_2005,
	title        = {Learning and {Linking} {Textual} {Cases}},
	author       = {Minor, Mirjam and Biermann, Christina},
	year         = 2005,
	booktitle    = {{ICCBR} 2005 {Workshop} {Proceedings}},
	publisher    = {DePaul University, Chicago, USA},
	address      = {Chicago, Illinois},
	pages        = {128--137},
	editor       = {Br{\"u}ninghaus, Steffi}
}

@inproceedings{minor_case-acquisition_2005,
	title        = {Case-{Acquisition} and {Semantic} {Cross}-{Linking} for {Case}-{Based} {Experience} {Management} {Systems}},
	author       = {Minor, Mirjam and Biermann, Christina},
	year         = 2005,
	booktitle    = {Proceedings of the 2005 {IEEE} {International} {Conference} on {Information} {Reuse} and {Integration} ({IRI}-2005)},
	publisher    = {IEEE Systems, Man and Cycbernetics Society (SMC)},
	pages        = {433--438},
	editor       = {Zhang, Du and Khoshgoftaar, Taghi M and Shyu, Mei-Ling}
}

@techreport{schmalen_serviceorientierte_2005,
	title        = {Serviceorientierte {Architektur} als {Weiterentwicklung} der unternehmensinternen {Integrationsplattform}},
	author       = {Schmalen, Daniel},
	year         = 2005,
	institution  = {Diplomarbeit Universit{\"a}t Trier}
}

@inproceedings{fresmann_collaboration_2005,
	title        = {Collaboration {Patterns} for {Adaptive} {Software} {Engineering} {Processes}},
	author       = {Fre{\ss}mann, Andrea and Sauer, Thomas and Bergmann, Ralph},
	year         = 2005,
	booktitle    = {Self-{Organization} and {Autonomic} {Informatics} ({I})},
	publisher    = {IOS Press},
	address      = {Amsterdam, Netherlands},
	volume       = 135,
	pages        = {304--312},
	note         = {1-58603-577-0},
	editor       = {Czap, Hans and Unland, R. and Branki, C. and Tianfield, H.}
}

@article{bergmann_representation_2005,
	title        = {Representation in case-based reasoning},
	author       = {Bergmann, Ralph and Kolodner, Janet and Plaza, Enric},
	year         = 2005,
	journal      = {The Knowledge Engineering Review},
	volume       = 20,
	number       = 3,
	pages        = {209--213},
	url          = {http://journals.cambridge.org/action/displayIssue?jid=KER&volumeId=20&seriesId=0&issueId=03}
}

@inproceedings{minor_assistant_2005,
	title        = {Assistant {Agents} {With} {Personal} {Ontologies}},
	author       = {Minor, Mirjam},
	year         = 2005,
	booktitle    = {Proceedings of {AAMAS} 2005 {Workshop} {AMKM}-2005},
	publisher    = {Universiteit Utrecht, The Netherlands},
	address      = {Utrecht, The Netherlands},
	pages        = {41--51},
	editor       = {Diggelen, Juriaan van and Dignum, Virginia and Elst, Ludger van and Abecker, Andreas}
}

@inproceedings{hanft_low-effort_2005,
	title        = {A {Low}-{Effort}, {Collaborative} {Maintenance} {Model} for {Textual} {CBR}},
	author       = {Hanft, Alexandre and Minor, Mirjam},
	year         = 2005,
	booktitle    = {{ICCBR} 2005 {Workshop} {Proceedings}},
	publisher    = {DePaul University, Chicago, USA},
	address      = {Chicago, Illinois},
	pages        = {138 -- 149},
	editor       = {Br{\"u}ninghaus, Steffi}
}

@article{cox_case-based_2005,
	title        = {Case-based planning},
	author       = {Cox, Michael and Munoz-Avila, Hector and Bergmann, Ralph},
	year         = 2005,
	journal      = {The Knowledge Engineering Review},
	volume       = 20,
	number       = 3,
	pages        = {283--287},
	url          = {http://journals.cambridge.org/action/displayIssue?jid=KER&volumeId=20&seriesId=0&issueId=03}
}

@inproceedings{maximini_approximative_2005,
	title        = {Approximative {Retrieval} of {Attribute} {Dependent} {Generalized} {Cases}},
	author       = {Maximini, Rainer and Tartakovski, Alexander},
	year         = 2005,
	booktitle    = {In: {FGWM} 2003 {Workshop} {Wissens}- und {Erfahrungsmanagement}, {GI} {Workshop} {Woche} {LLWA}},
	url          = {http://www.wi2.uni-trier.de/publications/2005_fgwm_converter.pdf}
}

@book{althoff_wm_2005,
	title        = {{WM} 2005: {Professional} {Knowledge} {Management}},
	year         = 2005,
	publisher    = {DFKI Kaiserslautern},
	editor       = {Althoff, Klaus-Dieter and Dengel, Andreas and Bergmann, Ralph and Nick, Markus and Roth-Berghofer, Thomas}
}

@book{althoff_professional_2005,
	title        = {Professional {Knowledge} {Management}, {Third} {Biennial} {Conference}, {WM} 2005, {Kaiserslautern}, {Germany}, {April} 10-13, 2005, {Revised} {Selected} {Papers}},
	year         = 2005,
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Computer} {Science}},
	volume       = 3782,
	isbn         = {3-540-30465-7},
	editor       = {Althoff, Klaus-Dieter and Dengel, Andreas and Bergmann, Ralph and Nick, Markus and Roth-Berghofer, Thomas}
}

@techreport{fresmann_socio-economic_2005,
	title        = {Socio-economic {Study} on {User} {Needs}: {Advanced} {Mulit}-{Modal} {Intelligence} for {Remote} {Assietance} ({AMIRA}) - {Deliverable} {D}2.1.0},
	author       = {Fre{\ss}mann, Andrea and Bendeck, Fawsy},
	year         = 2005,
	pages        = 89,
	url          = {http://www.wi2.uni-trier.de/publications/2005_technicalReport_D210.pdf},
	abstract     = {The socio-economic study of user needs addresses the needs of mobile workers r in terms of computerised support when working remotely, as well as the type of support that their employer organisations are prepared to provide. The two domains representing emergency fire services, through the UK Fire Services College and the vehicle roadside assistance (breakdown repair etc.) sector, through the in-house Transport Engineering Works (TEW) of West Midlands Fire and Civil Defence Authority provide most of the input.},
	institution  = {Universit{\"a}t Trier, Lehrstuhl f{\"u}r Wirtschaftsinformatik II}
}

The socio-economic study of user needs addresses the needs of mobile workers r in terms of computerised support when working remotely, as well as the type of support that their employer organisations are prepared to provide. The two domains representing emergency fire services, through the UK Fire Services College and the vehicle roadside assistance (breakdown repair etc.) sector, through the in-house Transport Engineering Works (TEW) of West Midlands Fire and Civil Defence Authority provide most of the input.

@inproceedings{fresmann_collaborative_2005,
	title        = {Collaborative {Agent}-{Based} {Knowledge} {Support} for {Empirical} and {Knowledge}-{Intense} {Processes}},
	author       = {Fre{\ss}mann, Andrea and Maximini, Kerstin and Maximini, Rainer and Sauer, Thomas},
	year         = 2005,
	booktitle    = {Multiagent {System} {Technologies}, {Third} {German} {Conference}, {MATES} 2005, {Koblenz}, {Germany}, {September} 11-13, 2005, {Proceedings}},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Computer} {Science}},
	volume       = 3550,
	pages        = {235--236},
	isbn         = {3-540-28740-X},
	url          = {http://www.wi2.uni-trier.de/publications/2005_MATES_fressmann.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Eymann, Torsten and Kl{\"u}gl, Franziska and Lamersdorf, Winfried and Klusch, Matthias and Huhns, Michael N.}
}

@inproceedings{fresmann_cbr-based_2005,
	title        = {{CBR}-based {Execution} and {Planning} {Support} for {Collaborative} {Workflows}},
	author       = {Fre{\ss}mann, Andrea and Maximini, Kerstin and Maximini, Rainer and Sauer, Thomas},
	year         = 2005,
	booktitle    = {6th {International} {Conference} on {Case}-{Based} {Reasoning}, {ICCBR} 2005, {Chicago}, {IL}, {USA}, {August} 23-26, 2005, {Workshop} {Proceedings}},
	publisher    = {Springer},
	pages        = {271--280},
	url          = {http://www.wi2.uni-trier.de/publications/2005_ICCBR05_fressmann.pdf},
	note         = 3075,
	editor       = {Br{\"u}ninghaus, Stefanie}
}

@inproceedings{tartakovski_applying_2005,
	title        = {Applying {Generalized} {Cases} to {Retrieval} and {Configuration} of {Life} {Insurance} {Policies}},
	author       = {Tartakovski, Alexander and Schaaf, Martin and Maximini, Rainer},
	year         = 2005,
	booktitle    = {Professional {Knowledge} {Management}: {Third} {Biennial} {Conference}, {WM} 2005, {Kaiserslautern}, {Germany}, {April} 10-13, 2005, {Revised} {Selected} {Papers}},
	publisher    = {Springer-Verlag GmbH},
	series       = {{LNAI}},
	volume       = 3782,
	pages        = {293--303},
	note         = {The original publication is available at www.springerlink.com; ISBN 3-540-30465-7},
	editor       = {Althoff, Klaus-Dieter and Dengel, Andreas and Bergmann, Ralph and Nick, Markus and Roth-Berghofer, Thomas}
}

@inproceedings{muscholl_workshop_2005,
	title        = {Workshop on {Current} {Aspects} of {Knowledge} {Management} in {Medicine} ({KMM}05)},
	author       = {Muscholl, Marita and Maximini, Kerstin},
	year         = 2005,
	booktitle    = {Professional {Knowledge} {Management}: {Third} {Biennial} {Conference}, {WM} 2005, {Kaiserslautern}, {Germany}, {April} 10-13, 2005, {Revised} {Selected} {Papers}},
	publisher    = {Springer-Verlag},
	series       = {{LNAI}},
	volume       = 3782,
	pages        = {316--318},
	url          = {http://www.wi2.uni-trier.de/publications/2005_KMM_LNCS_muscholl.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Althoff, Klaus-Dieter and Dengel, Andreas and Bergmann, Ralph and Nick, Markus and Roth-Berghofer, Thomas}
}

@inproceedings{minor_design_2005,
	title        = {Design of {Geologic} {Structure} {Models} with {Case} {Based} {Reasoning}},
	author       = {Minor, Mirjam and K{\"o}ppen, Sandro},
	year         = 2005,
	booktitle    = {{KI}2005: {Advances} in {Artificial} {Intelligence}. {Proceedings}},
	publisher    = {Springer-Verlag},
	series       = {{LNAI} 3698},
	pages        = {79--91},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Furbach, Ulrich}
}

@inproceedings{minor_introduction_2005,
	title        = {Introduction {Strategy} and {Feedback} from an {Experience} {Management} {Project}},
	author       = {Minor, Mirjam},
	year         = 2005,
	booktitle    = {Professional {Knowledge} {Management}. {WM} 2005 post-conference proceedings.},
	publisher    = {Springer-Verlag},
	series       = {{LNAI} 3782},
	pages        = {284 -- 292},
	url          = {http://www.wi2.uni-trier.de/publications/2005_WM_minor.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Althoff, Klaus-Dieter and Dengel, Andreas and Bergmann, Ralph and Nick, Markus and Roth-Berghofer, Thomas}
}

@inproceedings{fresmann_towards_2005,
	title        = {Towards {Collaborative} {Agent}-{Based} {Knowledge} {Support} for {Time}-{Critical} and {Business}-{Critical} {Processes}},
	author       = {Fre{\ss}mann, Andrea and Maximini, Rainer and Sauer, Thomas},
	year         = 2005,
	booktitle    = {Professional {Knowledge} {Management}: {Third} {Biennial} {Conference}, {WM} 2005, {Kaiserslautern}, {Germany}, {April} 10-13, 2005, {Revised} {Selected} {Papers}},
	publisher    = {Springer-Verlag GmbH},
	series       = {{LNAI}},
	volume       = 3782,
	pages        = {420--430},
	url          = {http://www.wi2.uni-trier.de/publications/2005_KMDAP_WM_postproceedings_fressmann.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Althoff, Klaus-Dieter and Dengel, Andreas and Bergmann, Ralph and Nick, Markus and Roth-Berghofer, Thomas}
}

@inproceedings{auriol_amira:_2005,
	title        = {{AMIRA}: {Supporting} {Diagnostics} {And} {Decisions} {In} {Safety}-{Critical} {Incidents}},
	author       = {Auriol, Eric and Heisterkamp, Paul and Aarberg, Ingvar and Diamond, Ben and Taylor, Brian and Fre{\ss}mann, Andrea},
	year         = 2005,
	booktitle    = {Innovation and the {Knowledge} {Economy}: {Issues}, {Applications}, {Case} {Studies}},
	publisher    = {2005 IOS Press Amsterdam},
	note         = {ISBN: 1-58603-563-0},
	editor       = {Cunningham, Paul and Cunningham, Miriam}
}

@inproceedings{tartakovski_retrieval_2005,
	title        = {Retrieval and {Configuration} of {Life} {Insurance} {Policies}},
	author       = {Tartakovski, Alexander and Schaaf, Martin and Bergmann, Ralph},
	year         = 2005,
	booktitle    = {Sixth {International} {Conference} on {Case}-{Based} {Reasoning} ({ICCBR} 2005)},
	publisher    = {Springer},
	address      = {Chicago, Illinois (USA)},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	volume       = 3620,
	pages        = {552--565},
	url          = {http://www.wi2.uni-trier.de/publications/2005_ICCBR.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Munoz-Avila, Hector and Ricci, F.}
}

@article{fresmann_intelligent_2004,
	title        = {Intelligent {Retrieval} for {Component} {Reuse} in {System}-{On}-{Chip} {Design}},
	author       = {Fre{\ss}mann, Andrea and Maximini, Rainer and Schaaf, Martin and Franz, Jasmin and Traph{\"o}ner, Ralph},
	year         = 2004,
	journal      = {KI - K{\"u}nstliche Intelligenz},
	number       = 2,
	pages        = {45--48},
	url          = {http://www.wi2.uni-trier.de/publications/2004_KI_fressmann.pdf}
}

@inproceedings{minor_experiences_2004,
	title        = {Experiences {From} an {Experience} {Management} {Project}},
	author       = {Minor, Mirjam and Altmeyer, Benjamin},
	year         = 2004,
	booktitle    = {Proc. {CSuP}-2004},
	address      = {Caputh, Germany},
	pages        = {496--504},
	editor       = {al., Gabriela Lindemann et}
}

@article{schaaf_intelligent_2004,
	title        = {Intelligent {IP} {Retrieval} {Driven} by {Application} {Requirements}},
	author       = {Schaaf, Martin and Fre{\ss}mann, Andrea and Maximini, Rainer and Bergmann, Ralph and Tartakovski, Alexander and Radetzki, Martin},
	year         = 2004,
	journal      = {Integration, the VLSI Journal},
	volume       = 37,
	number       = 4,
	pages        = {253--287},
	url          = {http://www.wi2.uni-trier.de/publications/2004_VLSI_schaaf.pdf}
}

@inproceedings{sauer_decision_2004,
	title        = {Decision {Support} for {Software} {Planning} and {Enactment}},
	author       = {Sauer, Thomas and Goldmann, Sigrid},
	year         = 2004,
	booktitle    = {Proceedings of the 1st {International} {Conference} on {Knowledge} {Engineering} and {Decision} {Support} ({ICKEDS}'04)},
	pages        = {79--86},
	note         = {ISBN 972-8688-24-5}
}

@techreport{thimm_amira_2004,
	title        = {{AMIRA} - {A} {State} of the {Art} {Analysis} of {Workflow} {Modelling} and {Agent}-based {Information}-{Systems}},
	author       = {Thimm, Rouven and Will, Thomas},
	year         = 2004,
	month        = oct,
	url          = {http://www.wi2.uni-trier.de/publications/2004_TechnicalReport_thimmWill.pdf},
	institution  = {University of Trier, Department of Business Information Systems II}
}

@inproceedings{mougouie_polynomial_2004,
	title        = {Polynomial {Algorithms} for {MPSP} {Using} {Parametric} {Linear} {Programming}},
	author       = {Mougouie, Babak},
	year         = 2004,
	booktitle    = {Logic versus {Approximation}, {Essays} {Dedicated} to {Michael} {M}. {Richter} on the {Occasion} of {His} 65th {Birthday}},
	publisher    = {Springer},
	volume       = 3075,
	pages        = {33--42},
	url          = {http://www.wi2.uni-trier.de/publications/2004_Scheduling_mougouie.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Lenski, Wolfgang}
}

@inproceedings{maximini_explanation_2004,
	title        = {Explanation {Service} for {Complex} {CBR} {Applications}},
	author       = {Maximini, Rainer and Fre{\ss}mann, Andrea and Schaaf, Martin},
	year         = 2004,
	month        = aug,
	booktitle    = {Advances in {Case}-{Based} {Reasoning}, {Proceedings} of the 7th {European} {Conference} on {Case} {Based} {Reasoning}, {ECCBR} 2004},
	publisher    = {Springer Verlag, Berlin-Heidelberg},
	address      = {Madrid, Spain},
	series       = {{LNAI}},
	volume       = 3155,
	pages        = {302--316},
	url          = {http://www.wi2.uni-trier.de/publications/2004_ECCBR_maximini.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Funk, Peter and Calero, Pedro A. Gonz{\'a}lez}
}

@inproceedings{minor_experience_2004,
	title        = {Experience {Management} with {Automatic} {Case} {Acquisition} and {Cross}-{Linking}},
	author       = {Minor, Mirjam and Bell, Christina},
	year         = 2004,
	booktitle    = {Proc. {LWA}-2004},
	address      = {Berlin},
	editor       = {al., Andreas Abecker et}
}

@inproceedings{bendeck_three_2004,
	title        = {Three {Fold} "{Ontology} + {Model} + {Instance} ({OMI})" {Semantic} {Unification} {Process}},
	author       = {Bendeck, Fawsy},
	year         = 2004,
	month        = sep,
	booktitle    = {International {Conference} on {Advances} in {Internet}, {Processing}, {System} and {Interdisiplinary} {Research} ({IPSI}-2004))},
	publisher    = {ISBN-86-7466-1173},
	address      = {Stockholm}
}

@article{sauer_decision_2004-1,
	title        = {Decision {Support} for {Software} {Planning} and {Enactment}},
	author       = {Sauer, Thomas and Goldmann, Sigrid},
	year         = 2004,
	journal      = {International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications, Special Issue on Decision Support}
}

@book{abecker_workshop_2004,
	title        = {Workshop der {GI}-{Fachgruppe} {FGWM}},
	year         = 2004,
	address      = {Berlin},
	editor       = {Abecker, Andreas and Minor, Mirjam and Stojanovic, Ljiljana}
}

@inproceedings{tartakovski_minlp_2004,
	title        = {{MINLP} {Based} {Retrieval} of {Generalized} {Cases}, {Proceedings} of the 7th {European} {Conference} on {Case} {Based} {Reasoning}, {ECCBR} 2004},
	author       = {Tartakovski, Alexander and Schaaf, Martin and Maximini, Rainer and Bergmann, Ralph},
	year         = 2004,
	month        = aug,
	booktitle    = {Advances in {Case}-{Based} {Reasoning}, 7th {European} {Conference}, {ECCBR} 2004, {Madrid}, {Spain}, {August} 30 - {September} 2, 2004},
	publisher    = {Springer},
	address      = {Madrid, Spain},
	series       = {{LNAI}},
	volume       = 3155,
	pages        = {404--418},
	url          = {http://www.wi2.uni-trier.de/publications/2004_tartakovskiECCBR.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Funk, Peter and Calero, Pedro A. Gonz{\'a}lez}
}

@inproceedings{balaa_fm-ultranet:_2003,
	title        = {{FM}-{Ultranet}: a decision support system using case-based reasoning, applied to ultrasonography},
	author       = {Balaa, Ziad El and Strauss, Anne and Maximini, Kerstin},
	year         = 2003,
	booktitle    = {In {Workshop} on {CBR} in the {Health} {Sciences}},
	url          = {http://www.wi2.uni-trier.de/publications/2003_ICCBR_WS_elBalaa.pdf}
}

@inproceedings{andrade_strategy_2003,
	title        = {A {Strategy} for a wireless patient record and image data},
	author       = {Andrade, Rafael and Wangenheim, Aldo von and Bortoluzzi, Mariana Kessler and Biasi, Herculano de},
	year         = 2003,
	booktitle    = {{CARS} 2003: {COMPUTER} {ASSISTED} {RADIOLOGY} {AND} {SURGERY}, {Proceedings} of the 17th {International} {Congress} and {Exhibition} - {Computer} assisted radiology and surgery},
	publisher    = {Elsevier Science B.V., Amsterdam},
	address      = {Londres},
	pages        = {869--872}
}

@inproceedings{minor_making_2003,
	title        = {Making use of a system's treasury of experiences},
	author       = {Minor, Mirjam},
	year         = 2003,
	booktitle    = {Beitr{\"a}ge der {GI}-{Workshopwoche} "{Lehren}-{Lernen}-{Wissen}-{Adaptivit{\"a}t}"},
	publisher    = {Universit{\"a}t Karlsruhe},
	address      = {Karlsruhe},
	pages        = {210 -- 215}
}

@inproceedings{tartakovski_similarity_2003,
	title        = {Similarity {Assessment} and {Retrieval} of {Generalized} {Cases}},
	author       = {Tartakovski, Alexander and Tartakovski, Er and Maximini, Rainer},
	year         = 2003,
	booktitle    = {In {R}. {Bergmann} \& {M}. {Schaaf} ({Eds}.), {Proceedings} of the {Workshop} on knowledge and experience management},
	pages        = {195--206},
	url          = {http://www.wi2.uni-trier.de/publications/2003_tartakovskiFGWM.pdf}
}

@inproceedings{andrade_using_2003,
	title        = {Using mobile wireless devices for {Interactive} {Visualization} and {Analysis} of {DICOM} {Data}},
	author       = {Andrade, Rafael and Wangenheim, Aldo von and Bortoluzzi, Mariana Kessler and Biasi, Herculano de},
	year         = 2003,
	booktitle    = {16th {IEEE} {Symposium} on {Computer}-{Based} {Medical} {Systems}, {Proceedings} of the 16th {IEEE} {Symposium} on {Computer}-{Based} {Medical} {Systems}},
	address      = {New York},
	pages        = {97--101}
}

@inproceedings{minor_experience_2003,
	title        = {Experience {Management} with {Knowledge} {Sharing} {Agents}},
	author       = {Minor, Mirjam and Wernicke, Mike},
	year         = 2003,
	booktitle    = {Forth {European} {Conference} on {Knowledge} {Management}},
	publisher    = {MCIL},
	address      = {Reading},
	pages        = {669--675},
	editor       = {McGrath, Fergal and Remenyi, Dan}
}

@inproceedings{sauer_using_2003,
	title        = {Using {Design} {Rationales} for {Agile} {Documentation}},
	author       = {Sauer, Thomas},
	year         = 2003,
	booktitle    = {Proceedings of the 12th {International} {Workshops} on {Enabling} {Technologies}: {Infrastructures} for {Collaborative} {Enterprises} ({WETICE}'03)},
	publisher    = {IEEE Computer Society Press},
	pages        = {326--331},
	note         = {ISBN 0-7695-1963-6}
}

@inproceedings{maximini_progemm_2003,
	title        = {The {PROGEMM} {Approach} for {Managing} {Clinical} {Processes}},
	author       = {Maximini, Kerstin and Schaaf, Martin},
	year         = 2003,
	month        = jun,
	booktitle    = {1st {Workshop} on {Knowledge} {Management} for {Distributed} {Agile} {Processes}: {Models}, {Techniques}, and {Infrastructure} ({KMDAP}'03) for the {Twelfth} {IEEE} {International} {Workshops} on {Enabling} {Technologies}: {Infrastructure} for {Collaborative} {Enterprises} ({WETICE}'03)},
	publisher    = {IEEE Computer Society, Los Alamitos, California},
	address      = {Linz, Austria},
	pages        = {332--337},
	url          = {http://www.wi2.uni-trier.de/publications/2003_KMDAP-maximini.pdf},
	editor       = {Kotsis, Gabriele and Reddy, Sumitra}
}

@book{nick_iccbr-03_2003,
	title        = {{ICCBR}-03 {Workshop} on {Long}-{Lived} {CBR} {Systems}},
	year         = 2003,
	publisher    = {Norwegian University of Science and Technology},
	address      = {Trondheim, Norway},
	editor       = {Nick, Markus and Minor, Mirjam}
}

@inproceedings{minor_exchange_2003,
	title        = {The {Exchange} of {Retrieval} {Knowledge} about {Services} between {Agents}},
	author       = {Minor, Mirjam and Wernicke, Mike},
	year         = 2003,
	booktitle    = {Beitr{\"a}ge der 2. {Konferenz} {Professionelles} {Wissensmanagement} - {Erfahrungen} und {Visionen} ({WM} 2003)},
	publisher    = {Gesellschaft f{\"u}r Informatik (GI)},
	address      = {Bonn},
	pages        = {295 -- 303},
	note         = {Lecture Notes in Informatics, Vol. P-28},
	editor       = {al., Ulrich Reimer et}
}

@inproceedings{maximini_investigating_2003,
	title        = {Investigating different {Methods} for efficient {Retrieval} of {Generalized} {Cases}},
	author       = {Maximini, Rainer and Tartakovski, Alexander and Bergmann, Ralph},
	year         = 2003,
	booktitle    = {{WM} 2003: {Professionelles} {Wissesmanagement} - {Erfahrungen} und {Visionen}, {Beitr{\"a}ge} der 2. {Konferenz} {Professionelles} {Wissensmanagement}, 2.-4. {April} 2003 in {Luzern}},
	publisher    = {GI},
	series       = {{LNI}},
	volume       = 28,
	pages        = {303--304},
	isbn         = {3-88579-357-1},
	url          = {http://www.wi2.uni-trier.de/publications/2003_gwem.pdf},
	editor       = {Reimer, Ulrich and Abecker, Andreas and Staab, Steffen and Stumme, Gerd}
}

@article{bergmann_structural_2003,
	title        = {Structural {Case}-{Based} {Reasoning} and {Ontology}-based {Knowledge} {Managemenet}: {A} {Perfect} {Match}?},
	author       = {Bergmann, Ralph and Schaaf, Martin},
	year         = 2003,
	journal      = {Journal of Universal Computer Science},
	volume       = 9,
	number       = 7,
	url          = {http://www.wi2.uni-trier.de/publications/2003_danab_Schaaf_Bergmann.pdf}
}

@inproceedings{bortoluzzi_clinical_2003,
	title        = {A {Clinical} {Report} {Management} {System} {Based} upon the {DICOM} {Structured} {Report} {Standard}},
	author       = {Bortoluzzi, Mariana Kessler and Wangenheim, Aldo von and Maximini, Kerstin},
	year         = 2003,
	booktitle    = {16th {IEEE} {Symposium} on {Computer}-{Based} {Medical} {Systems} ({CBMS} 2003), 26-27 {June} 2003, {New} {York}, {NY}, {USA}},
	publisher    = {IEEE Computer Society},
	pages        = {183--188},
	isbn         = {0-7695-1901-6},
	url          = {http://www.wi2.uni-trier.de/publications/2003_CBMS_bortoluzzi.pdf}
}

@inproceedings{alexandrini_integrating_2003,
	title        = {Integrating {CBR} into the {Health} {Care} {Organization}},
	author       = {Alexandrini, F{\'a}bio and Krechel, Dirk and Maximini, Kerstin and Wangenheim, Aldo von},
	year         = 2003,
	booktitle    = {16th {IEEE} {Symposium} on {Computer}-{Based} {Medical} {Systems} ({CBMS} 2003), 26-27 {June} 2003, {New} {York}, {NY}, {USA}},
	publisher    = {IEEE Computer Society},
	pages        = {130--135},
	isbn         = {0-7695-1901-6},
	url          = {http://www.wi2.uni-trier.de/publications/2003_CBMS_alexandrini.pdf}
}

@inproceedings{mougouie_generalized_2003,
	title        = {Generalized {Cases}, {Similarity} and {Optimization}},
	author       = {Mougouie, Babak and Richter, Michael M},
	year         = 2003,
	booktitle    = {Deduction and beyond},
	publisher    = {LNAI 2605, Springer-Verlag},
	url          = {http://www.wi2.uni-trier.de/publications/2003_Deduction_mougouie.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Hutter, D. and Stephan, W.}
}

@article{minor_buchrezension_2003,
	title        = {Buchrezension zu {Experience} {Management}},
	author       = {Minor, Mirjam},
	year         = 2003,
	journal      = {Zeitschrift KI},
	volume       = {03},
	number       = 3,
	pages        = 78
}

@book{bergmann_developing_2003,
	title        = {Developing {Industrial} {Case}-{Based} {Reasoning} {Applications}: {The} {INRECA} {Methodology}. (2nd {Edition}).},
	author       = {Bergmann, Ralph and Althoff, Klaus-Dieter and Breen, Sean and G{\"o}ker, Mehmet and Manago, Michel and Traph{\"o}ner, Ralph and Wess, Stefan},
	year         = 2003,
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	volume       = 1612,
	url          = {http://www.amazon.de/Developing-Industrial-Case-Based-Reasoning-Applications/dp/3540207376/ref=sr_1_1?ie=UTF8&qid=1309873607&sr=8-1},
	note         = {The original publication is available at www.springerlink.com},
	edition      = 2
}

@inproceedings{bergmann_relations_2003,
	title        = {On the {Relations} between {Structural} {Case}-{Based} {Reasoning} and {Ontology}-based {Knowledge} {Management}},
	author       = {Bergmann, Ralph and Schaaf, Martin},
	year         = 2003,
	booktitle    = {{WM} 2003: {Professionelles} {Wissesmanagement} - {Erfahrungen} und {Visionen}, {Beitr{\"a}ge} der 2. {Konferenz} {Professionelles} {Wissensmanagement}, 2.-4. {April} 2003 in {Luzern}},
	publisher    = {GI},
	series       = {{LNI}},
	volume       = 28,
	pages        = {279--286},
	url          = {http://www.wi2.uni-trier.de/publications/2003_GWEM-OntoCBR-CRC.pdf},
	editor       = {Reimer, Ulrich and Abecker, Andreas and Staab, Steffen and Stumme, Gerd}
}

@inproceedings{maximini_investigation_2003,
	title        = {An {Investigation} of {Generalized} {Cases}},
	author       = {Maximini, Kerstin and Maximini, Rainer and Bergmann, Ralph},
	year         = 2003,
	booktitle    = {Case-{Based} {Reasoning} {Research} and {Development}, 5th {International} {Conference} on {Case}-{Based} {Reasoning}, {ICCBR} 2003, {Trondheim}, {Norway}, {June} 23-26, 2003, {Proceedings}},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	volume       = 2689,
	pages        = {261--275},
	isbn         = {3-540-40433-3},
	url          = {http://www.wi2.uni-trier.de/publications/2003_ICCBR_maximini.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Ashley, Kevin D. and Bridge, Derek G.}
}

@inproceedings{mougouie_diversity-conscious_2003,
	title        = {Diversity-{Conscious} {Retrieval} from {Generalized} {Cases}: {A} {Branch} and {Bound} {Algorithm}},
	author       = {Mougouie, Babak and Richter, Michael M and Bergmann, Ralph},
	year         = 2003,
	booktitle    = {Case-{Based} {Reasoning} {Research} and {Development}, 5th {International} {Conference} on {Case}-{Based} {Reasoning}, {ICCBR} 2003, {Trondheim}, {Norway}, {June} 23-26, 2003},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	volume       = 2989,
	pages        = {319--331},
	url          = {http://www.wi2.uni-trier.de/publications/2003_ICCBR_mougouie.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Ashley, Kevin D and Bridge, Derek}
}

@inproceedings{schaaf_knowledge_2003,
	title        = {A {Knowledge} {Representation} {Format} for {Virtual} {IP} {Marketplaces}},
	author       = {Schaaf, Martin and Fre{\ss}mann, Andrea and Spinelli, Marco A. and Maximini, Rainer and Bergmann, Ralph},
	year         = 2003,
	booktitle    = {Case-{Based} {Reasoning} {Research} and {Development}, 5th {International} {Conference} on {Case}-{Based} {Reasoning}, {ICCBR} 2003, {Trondheim}, {Norway}, {June} 23-26, 2003},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	volume       = 2689,
	pages        = {509--521},
	isbn         = {3-540-40433-3},
	url          = {http://www.wi2.uni-trier.de/publications/2003_ICCBR_schaaf.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Ashley, Kevin D. and Bridge, Derek G.}
}

@inproceedings{wiese_case-based_2003,
	title        = {A {Case}-{Based} {Predictive} {Sequencing} {Batch} {Reactor} {Controller}},
	author       = {Wiese, J{\"u}rgen and Schmitt, Sascha and Bergmann, Ralph and Schmitt, Theo G},
	year         = 2003,
	booktitle    = {18th {International} {Joint} {Conference} on {Artificial} {Intelligence}, {Workshop} on {Environmental} {Decision} {Support} {Systems} ({EDSS}'2003)},
	url          = {http://www.wi2.uni-trier.de/publications/2003_WieseEtAl_EDSS_IJCAI.pdf}
}

@article{bergmann_acquiring_2002,
	title        = {Acquiring {Customers}' {Requirements} in {Electronic} {Commerce}},
	author       = {Bergmann, Ralph and Cunningham, P{\'a}draig},
	year         = 2002,
	journal      = {Artificial Intelligence Review},
	volume       = 18,
	number       = {3-4},
	pages        = {163--193},
	url          = {http://www.wi2.uni-trier.de/publications/2002_AIRBergCun_fin_.pdf}
}

@inproceedings{ribeiro_cyclopsdistmeddb._2002,
	title        = {{CyclopsDistMedDB}. - {A} {Transparent} {Gateway} for {Distributed} {Medical} {Data} {Access} in {DICOM} {Format}},
	author       = {Ribeiro, Leonardo Andrade and Dellani, Paulo Roberto and Wangenheim, Aldo von and Richter, Michael M and Maximini, Kerstin and Comunello, Eros},
	year         = 2002,
	month        = jun,
	booktitle    = {Proceedings of the 15th {IEEE} {Symposium} on {Computer}-based {Medical} {Systems} ({CBMS}'02)},
	publisher    = {IEEE Computer Society, Los Alamitos, California},
	address      = {Maribor, Slovenia},
	pages        = {315--320},
	editor       = {Kokol, Peter and Stiglic, Bruno and Zorman, Milan and Zazula, Damjan}
}

@inproceedings{krechel_framework_2002,
	title        = {A {Framework} for {Radiological} {Assistant} {Systems}},
	author       = {Krechel, Dirk and Bergmann, Ralph and Maximini, Kerstin and Wangenheim, Aldo von},
	year         = 2002,
	month        = jun,
	booktitle    = {Proceedings of the 15th {IEEE} {Symposium} on {Computer}-based {Medical} {Systems} ({CBMS}'02)},
	publisher    = {IEEE Computer Society, Los Alamitos, California},
	address      = {Maribor, Slovenia},
	pages        = {179--184},
	url          = {http://www.wi2.uni-trier.de/publications/2002_cbms.pdf},
	note         = {Best Paper Award},
	editor       = {Kokol, Peter and Stiglic, Bruno and Zorman, Milan and Zazula, Damjan}
}

@inproceedings{chen_semistructured_2002,
	title        = {Semistructured {Data} {Store} {Mapping} with {XML} and its {Reconstruction}},
	author       = {Chen, Enhong and Wu, Gongqing and Lindemann, Gabriela and Minor, Mirjam},
	year         = 2002,
	booktitle    = {{ADBIS} 2002, {Vol}. 2: {Research} {Communications}},
	publisher    = {Slowak University of Technology},
	address      = {Bratislava},
	pages        = {78 -- 87},
	editor       = {Manololoulos, Yannis and Navrat, Pavol}
}

@inproceedings{stahl_customization_2002,
	title        = {A {Customization} {Approach} for {Structured} {Products} in {Electronic} {Shops}},
	author       = {Stahl, Armin and Bergmann, Ralph and Schmitt, Sascha},
	year         = 2002,
	booktitle    = {13th {International} {Bled} {Electronic} {Commerce} {Conference}},
	url          = {http://www.wi2.uni-trier.de/publications/2002_Bled.pdf}
}

@inproceedings{bergmann_experience_2002,
	title        = {Experience {Management} for {Electronic} {Design} {Reuse} through {Quality}-{Oriented} {IP} {Selection}},
	author       = {Bergmann, Ralph and Maximini, Rainer and Schaaf, Martin},
	year         = 2002,
	booktitle    = {1st {German} {Workshop} on {Experience} {Management}: {Sharing} {Experiences} about the {Sharing} of {Experience}, {Berlin}, {March} 7-8, 2002, {Proceedings}},
	publisher    = {GI},
	series       = {{LNI}},
	volume       = 10,
	pages        = {11--22},
	isbn         = {3-88579-340-7},
	url          = {http://www.wi2.uni-trier.de/publications/2002_bergmannmaximinischaaf.pdf},
	editor       = {Minor, Mirjam and Staab, Steffen}
}

@inproceedings{minor_communication_2002,
	title        = {The {Communication} of a {System}'s {Treasury} of {Experience}},
	author       = {Minor, Mirjam},
	year         = 2002,
	month        = sep,
	booktitle    = {{ECCBR}'02 {Workshop} {Proc}.},
	publisher    = {University of Aberdeen}
}

@techreport{tautz_prototypical_2002,
	title        = {Prototypical implementation of the {Quality} {Related} {Retrieval}. {IPQ} {Deliverable} {AP}2-{T}1-{Q}4},
	author       = {Tautz, Carsten and Traph{\"o}ner, Ralph and Bergmann, Ralph and Maximini, Rainer and Schaaf, Martin},
	year         = 2002,
	institution  = {University of Hildesheim}
}

@techreport{tautz_knowledge_2002,
	title        = {Knowledge {Representation} for {IP} {Retrieval}. {IPQ} {Deliverable} {AP}2-{T}2-{Q}4},
	author       = {Tautz, Carsten and Traph{\"o}ner, Ralph and Bergmann, Ralph and Maximini, Rainer and Schaaf, Martin},
	year         = 2002,
	institution  = {University of Hildesheim}
}

@inproceedings{bortoluzzi_um_2002,
	title        = {Um {Editor} de {Laudos} no {Padr}{\textbackslash} ao {DICOM} {Structured} {Report}},
	author       = {Bortoluzzi, Mariana Kessler and Wangenheim, Aldo von and Sehn, Michel and Caregnato, Maricy},
	year         = 2002,
	booktitle    = {Anais do {VIII} {Congresso} {Brasileiro} de {Inform{\'a}tica} em {Sa{\'u}de}},
	address      = {Natal}
}

@article{minor_buchrezension_2002,
	title        = {Buchrezension zum {Handbuch} der {K{\"u}nstlichen} {Intelligenz}},
	author       = {Minor, Mirjam},
	year         = 2002,
	journal      = {Zeitschrift KI},
	volume       = {02},
	number       = 4
}

@book{bergmann_experience_2002-1,
	title        = {Experience {Management}: {Foundations}, {Development} {Methodology}, and {Internet}-{Based} {Applications}},
	author       = {Bergmann, Ralph},
	year         = 2002,
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	volume       = 2432,
	url          = {http://www.amazon.de/Experience-Management-Foundations-Internet-Based-Applications/dp/3540441913/ref=sr_1_1?ie=UTF8&qid=1309857671&sr=8-1},
	note         = {The original publication is available at www.springerlink.com}
}

@incollection{bergmann_knowledge-intensive_2002,
	title        = {Knowledge-intensive product search and customization in electronic commerce.},
	author       = {Bergmann, Ralph and Traph{\"o}ner, Ralph and Schmitt, Sascha and Cunningham, P{\'a}draig and Smyth, Barry},
	year         = 2002,
	booktitle    = {E-{Business} {Applications}},
	publisher    = {Springer},
	series       = {Advanced {Information} {Systems}},
	pages        = {87--101},
	url          = {http://www.wi2.uni-trier.de/publications/2002_bergmann et al. Buchbeitrag.pdf},
	note         = {The original publication is available at www.springerlink.com}
}

@incollection{bergmann_intelligent_2002,
	title        = {Intelligent customer support for product selection with case-based reasoning.},
	author       = {Bergmann, Ralph and Schmitt, Sascha and Stahl, Armin},
	year         = 2002,
	booktitle    = {E-{Commerce} and {Intelligent} {Methods}},
	publisher    = {Physica-Verlag},
	pages        = {322--341},
	url          = {http://www.wi2.uni-trier.de/publications/2002_bergmannschmittstahl.pdf},
	editor       = {Segovia, J. and Szezepaniak, P. and Niedzwiedzinski, M.}
}

@inproceedings{mougouie_similarity_2002,
	title        = {Similarity {Assessment} for {Generalized} {Cases} by {Optimization} {Methods}},
	author       = {Mougouie, Babak and Bergmann, Ralph},
	year         = 2002,
	booktitle    = {European {Conference} on {Case}-{Based} {Reasoning} ({ECCBR}'02)},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	volume       = 2416,
	pages        = {249--263},
	url          = {http://www.wi2.uni-trier.de/publications/2002_ECCBR_mougouie.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Craw, S. and Preece, A.}
}

@inproceedings{schaaf_supporting_2002,
	title        = {Supporting {Electronic} {Design} {Reuse} by {Integrating} {Quality}-{Criteria} into {CBR}-{Based} {IP} {Selection}},
	author       = {Schaaf, Martin and Maximini, Rainer and Bergmann, Ralph and Tautz, Carsten and Traph{\"o}ner, Ralph},
	year         = 2002,
	booktitle    = {Advances in {Case}-{Based} {Reasoning}, 6th {European} {Conference}, {ECCBR} 2002 {Aberdeen}, {Scotland}, {UK}, {September} 4-7, 2002, {Proceedings}},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	volume       = 2416,
	pages        = {628--641},
	isbn         = {3-540-44109-3},
	url          = {http://www.wi2.uni-trier.de/publications/2002_eccbr.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Craw, Susan and Preece, Alun D.}
}

@incollection{schaaf_ipchl_2002,
	title        = {{IPCHL} - {A} {Description} {Language} for {Semantic} {IP} {Characterization}},
	author       = {Schaaf, Martin and Visarius, Markus and Bergmann, Ralph and Maximini, Rainer and Spinelli, Marco and Lessmann, Johannes and Hardt, Wolfram and Thronicke, Wolfgang and Franz, Jasmin and Tautz, Carsten and Traph{\"o}ner, Ralph},
	year         = 2002,
	month        = sep,
	booktitle    = {Best of {FDL}'02, {System} {Specification} \& {Design} {Languages}},
	url          = {http://www.wi2.uni-trier.de/publications/2002_fdl.pdf}
}

@inproceedings{kohlmaier_evaluation_2001,
	title        = {Evaluation of a similarity-based approach to customer-adaptive electronic sales dialogs},
	author       = {Kohlmaier, Andreas and Schmitt, Sascha and Bergmann, Ralph},
	year         = 2001,
	month        = jul,
	booktitle    = {Workshop on {Empirical} {Evaluations} of {Adaptive} {Systems}},
	pages        = {41--50},
	url          = {http://www.wi2.uni-trier.de/publications/2001_kohlmaier_et_al.pdf},
	editor       = {Weibelzahl, Stephan and Chin, David and Weber, Gehard}
}

@techreport{tautz_concepts_2001,
	title        = {Concepts for the {Integration} of {IP} {Quality} {Criteria} into {IP} {Characterization} and {Retrieval}. {IPQ} {Deliverable} {AP}2-{T}1-{Q}2},
	author       = {Tautz, Carsten and Traph{\"o}ner, Ralph and Bergmann, Ralph and Maximini, Rainer and Schaaf, Martin and Vollrath, Ivo},
	year         = 2001,
	institution  = {University of Kaiserslautern}
}

@inproceedings{minor_experience_2001,
	title        = {Experience {Management} - {Case} {Based} {Reasoning} for {Knowledge} {Management}},
	author       = {Minor, Mirjam},
	year         = 2001,
	booktitle    = {Proc. {CSuP}-2001},
	address      = {Warsaw},
	pages        = {150--159},
	editor       = {Czaja, Ludwik}
}

@inproceedings{schaaf_using_2001,
	title        = {Using {MILOS} {For} {Dependency} {Management} {In} {UML}-{Based} {SE}-{Processes}},
	author       = {Schaaf, Martin and Bendeck, Fawsy and Nour, Philip},
	year         = 2001,
	month        = jun,
	booktitle    = {10th {IEEE} {International} {Workshops} on {Enabling} {Technologies}: {Infrastructure} for {Collaborative} {Enterprises} ({WETICE} 2001)},
	publisher    = {IEEE Computer Society},
	pages        = 18
}

@inproceedings{schmitt_formal_2001,
	title        = {A {Formal} {Approach} to {Dialogs} with {Online} {Customers}},
	author       = {Schmitt, Sascha and Bergmann, Ralph},
	year         = 2001,
	booktitle    = {Proc. of the 14 th {Bled} {Electronic} {Commerce} {Conference}},
	url          = {http://www.wi2.uni-trier.de/publications/2001_schmittbergmann2001.pdf}
}

@inproceedings{bendeck_engineering_2001,
	title        = {Engineering of e-{Business} {Applications} \& {Infrastructure} and {Applications} for the {Mobile} {Internet}.},
	author       = {Bendeck, Fawsy and K{\"o}tting, Boris and Schaaf, Martin and Maurer, Frank and Valenti, Matthew and Robert, Max},
	year         = 2001,
	month        = jun,
	booktitle    = {10th {IEEE} {International} {Workshops} on {Enabling} {Technologies}: {Infrastructure} for {Collaborative} {Enterprises} ({WETICE} 2001)},
	publisher    = {IEEE Computer Society},
	address      = {Cambridge, Massachusetts},
	pages        = {7--11}
}

@inproceedings{bergmann_utility-oriented_2001,
	title        = {Utility-{Oriented} {Matching}: {A} {New} {Research} {Direction} for {Case}-{Based} {Reasoning}},
	author       = {Bergmann, Ralph and Richter, Michael M and Schmitt, Sascha and Stahl, Armin and Vollrath, Ivo},
	year         = 2001,
	booktitle    = {9th {German} {Workshop} on {Case}-{Based} {Reasoning} ({GWCBR}'2001)},
	url          = {http://www.wi2.uni-trier.de/publications/2001_bergmannrichter.pdf}
}

@article{minor_textual_2001,
	title        = {Textual {CBR} im {E}-{Commerce}},
	author       = {Minor, Mirjam and Lenz, Mario},
	year         = 2001,
	journal      = {Zeitschrift KI},
	volume       = {01},
	number       = 1,
	pages        = {12 -- 16},
	url          = {http://www.wi2.uni-trier.de/publications/2001_KI_MinorLenz.pdf}
}

@inproceedings{minor_flexible_2001,
	title        = {Flexible {Strategies} for {Maintaining} {Knowledge} {Containers}: {ECAI}-2000 {Workshop} {Summary}},
	author       = {Minor, Mirjam and Wilson, David},
	year         = 2001,
	booktitle    = {Proc. of the {Workshop} {Program} at {ICCBR} 2001},
	publisher    = {Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence},
	address      = {Washington, DC},
	volume       = {Technical Note AIC-01-003},
	pages        = {64--67},
	editor       = {Weber, Rosina and Wangenheim, Christiane Gresse von}
}

@inproceedings{minor_multilingual_2001,
	title        = {Multilingual {Textual} {Case}-{Based} {Reasoning}},
	author       = {Minor, Mirjam and Prado, Jose Carlos Del},
	year         = 2001,
	booktitle    = {Professionelles {Wissensmanagement}: {Erfahrungen} und {Visionen}},
	publisher    = {Shaker-Verlag},
	address      = {Aachen},
	pages        = {281--282},
	editor       = {Schnurr, Hans-Peter and Staab, Steffen and Studer, Rudi and Stumme, Gerd and Sure, York}
}

@techreport{tautz_requirements_2001,
	title        = {Requirements on {IP} {Qualifying} {Format}. {IPQ} {Deliverable} {AP}1-{T}1-{Q}2},
	author       = {Tautz, Carsten and Traph{\"o}ner, Ralph and R{\"u}lke, Stefan and Martinez, N. and Seepolt, Ralf and V{\"o}rg, A. and Radetzki, Martin and Thronicke, Wolfgang and Jerinic, V. and M{\"u}ller, D. and Siegmund, M. and Bergmann, Ralph and Vollrath, Ivo and Schaaf, Martin},
	year         = 2001,
	institution  = {University of Kaiserslautern}
}

@inproceedings{bendeck_automation_2001,
	title        = {Automation of {XML} {Documents} {Translators} {Generation}},
	author       = {Bendeck, Fawsy},
	year         = 2001,
	month        = jun,
	booktitle    = {10th {IEEE} {International} {Workshops} on {Enabling} {Technologies}: {Infrastructure} for {Collaborative} {Enterprises} ({WETICE} 2001)},
	address      = {Cambridge, Massachusetts}
}

@article{bergmann_kunstliche_2001,
	title        = {K{\"u}nstliche {Intelligenz} und {Electronic} {Commerce}. {Special} {Issue} der {Zeitschrift} {K{\"u}nstliche} {Intelligenz}},
	year         = 2001,
	number       = {1/2001},
	url          = {http://www.kuenstliche-intelligenz.de/index.php?id=%3ANO-2016},
	editor       = {Bergmann, Ralph and Wilke, Wolfgang}
}

@inproceedings{cunningham_intelligent_2001,
	title        = {Intelligent {Support} for {Online} {Sales}: {The} {WEBSELL} {Experience}},
	author       = {Cunningham, P{\'a}draig and Bergmann, Ralph and Schmitt, Sascha and Traph{\"o}ner, Ralph and Breen, Sean and Smyth, Barry},
	year         = 2001,
	booktitle    = {Proceedings of the {Workshop} {Program} at the {Fourth} {International} {Conference} on {Case}-{Based} {Reasoning} ({ICCBR} 2001)},
	url          = {http://www.wi2.uni-trier.de/publications/2001_cunninghambergmann.pdf},
	note         = {Proceedings of the ECAI'2000 Workshop on Flexible Strategies for Maintaining Knowledge Containers},
	editor       = {Weber, R. and Wangenheim, Christiane Gresse von}
}

@inproceedings{bergmann_highlights_2001,
	title        = {Highlights of the {European} {INRECA} {Projects}},
	author       = {Bergmann, Ralph},
	year         = 2001,
	booktitle    = {Case-{Based} {Reasoning} {Research} and {Development}, 4th {International} {Conference} on {Case}-{Based} {Reasoning}, {ICCBR} 2001, {Vancouver}, {BC}, {Canada}, {July} 30 - {August} 2, 2001, {Proceedings}},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	volume       = 2080,
	pages        = {1--15},
	isbn         = {3-540-42358-3},
	url          = {http://www.wi2.uni-trier.de/publications/2001_Bergmann Inreca ICCBR.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Aha, David W. and Watson, Ian}
}

@inproceedings{kohlmaier_similarity-based_2001,
	title        = {A {Similarity}-{Based} {Approach} to {Attribute} {Selection} in {User}-{Adaptive} {Sales} {Dialogs}},
	author       = {Kohlmaier, Andreas and Schmitt, Sascha and Bergmann, Ralph},
	year         = 2001,
	booktitle    = {Case-{Based} {Reasoning} {Research} and {Development}, 4th {International} {Conference} on {Case}-{Based} {Reasoning}, {ICCBR} 2001, {Vancouver}, {BC}, {Canada}, {July} 30 - {August} 2, 2001, {Proceedings}},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	volume       = 2080,
	pages        = {306--320},
	isbn         = {3-540-42358-3},
	url          = {http://www.wi2.uni-trier.de/publications/2001_akohlmaier.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Aha, David W. and Watson, Ian}
}

@article{cunningham_websell:_2001,
	title        = {{WEBSELL}: {Intelligent} {Sales} {Assistants} for the {World} {Wide} {Web}},
	author       = {Cunningham, Padraig and Bergmann, Ralph and Schmitt, Sascha and Traph{\"o}ner, Ralph and Breen, Sean and Smyth, Barry},
	year         = 2001,
	journal      = {KI - K{\"u}nstliche Intelligenz},
	volume       = 15,
	number       = 1,
	pages        = {28--32},
	url          = {http://www.wi2.uni-trier.de/publications/2001_KI Websell.pdf}
}

@inproceedings{minor_multilingual_2000,
	title        = {Multilingual {Textual} {Case}-{Based} {Reasoning}},
	author       = {Minor, Mirjam and Prado, Jose Carlos Del},
	year         = 2000,
	booktitle    = {Proceedings of the {Workshop} {Concurrency}, {Specification} and {Programming} ({CS}\&{P} 2000)},
	publisher    = {Humboldt-Universit{\"a}t zu Berlin},
	address      = {Berlin},
	pages        = {143 -- 148},
	note         = {Informatik-Bericht Nr. 140, Vol. I},
	editor       = {Burkhard, Hans-Dieter and Czaja, Ludwik and Skowron, Andrzej and Starke, Peter}
}

@article{maurer_merging_2000,
	title        = {Merging {Project} {Planning} and {Web}-{Enabled} {Dynamic} {Workflow} {Technologies}},
	author       = {Maurer, Frank and Dellen, Barbara and Bendeck, Fawsy and Goldmann, Sigrid and Holz, Harald and K{\"o}tting, Boris and Schaaf, Martin},
	year         = 2000,
	journal      = {IEEE Internet Computing},
	volume       = 4,
	number       = 3,
	pages        = {65--74}
}

@inproceedings{stahl_customization_2000,
	title        = {A {Customization} {Approach} for {Structured} {Products} in {Electronic} {Shops}},
	author       = {Stahl, Armin and Bergmann, Ralph and Schmitt, Sascha},
	year         = 2000,
	booktitle    = {in {Proceedings} of the {Thirteenth} {Bled} {Electronic} {Commerce} {Conference}},
	pages        = {252--264},
	url          = {http://www.wi2.uni-trier.de/publications/2000_bled.pdf}
}

@inproceedings{koegst_utilizing_2000,
	title        = {Utilizing {Constraint} {Satisfaction} {Approaches} for {IP} {Retrieval}},
	author       = {Koegst, M. and Schneider, J. and Bergmann, Ralph and Vollrath, Ivo},
	year         = 2000,
	booktitle    = {3rd {Workshop} on {System} {Design} {Automation}},
	address      = {Rathen},
	url          = {http://www.wi2.uni-trier.de/publications/2000_sda.pdf}
}

@techreport{faber_object-oriented_2000,
	title        = {Object-{Oriented} {Implementation} of a {DICOM}-{Network} {Client} in {SMALLTALK}},
	author       = {Faber, Kerstin},
	year         = 2000,
	url          = {http://www.wi2.uni-trier.de/publications/2000_PA_faber.ps},
	type         = {Project {Thesis}},
	institution  = {University of Kaiserslautern, Germany}
}

@inproceedings{schmitt_implementation_2000,
	title        = {Implementation of a {Product} {Customization} {Module} for {CBR} {Systems} in {Electronic} {Commerce} {Environments}},
	author       = {Schmitt, Sascha and Maximini, Rainer and Landeck, Gerhard and Hohwiller, J{\"o}rg},
	year         = 2000,
	booktitle    = {Case-{Based} {Reasoning}: {Papers} from the {Eighth} {German} {Workshop}. {L{\"a}mmerbuckel}, {Germany}: {DaimlerChrysler} {Research} and {Technology}},
	url          = {http://www.wi2.uni-trier.de/publications/2000_gwcbr.pdf},
	editor       = {G{\"o}ker, Mehmet}
}

@inproceedings{schumacher_similarity-based_2000,
	title        = {Similarity-based retrieval on top of relational databases},
	author       = {Schumacher, J{\"u}rgen and Bergmann, Ralph},
	year         = 2000,
	booktitle    = {Case-{Based} {Reasoning}: {Papers} from the {Eighth} {German} {Workshop}. {L{\"a}mmerbuckel}, {Germany}: {DaimlerChrysler} {Research} and {Technology}},
	editor       = {G{\"o}ker, Mehmet}
}

@inproceedings{krechel_workflow_2000,
	title        = {A {Workflow} {Component} for {Knowledge} {Management} in the {Field} of {Radiological} {Examinations}},
	author       = {Krechel, Dirk and Faber, Kerstin and Reidenbach, Daniel and Blasinger, Klaus and Wangenheim, Aldo von and Comunello, Eros},
	year         = 2000,
	booktitle    = {Bildverarbeitung f{\"u}r die {Medizin}},
	pages        = {439--443},
	url          = {http://www.wi2.uni-trier.de/publications/2000_BVM_krechel.pdf}
}

@inproceedings{minor_corporate_2000,
	title        = {Corporate {Knowledge} {Editing} with a {Life} {Cycle} {Model}},
	author       = {Minor, Mirjam and Hanft, Alexandre},
	year         = 2000,
	month        = mar,
	booktitle    = {Case-{Based} {Reasoning}: {Papers} from the {Eighth} {German} {Workshop}. {L{\"a}mmerbuckel}, {Germany}: {DaimlerChrysler} {Research} and {Technology}},
	address      = {Ulm}
}

@inproceedings{stahl_applying_2000,
	title        = {Applying recursive {CBR} for the customization of structured products in an electronic shop},
	author       = {Stahl, Armin and Bergmann, Ralph},
	year         = 2000,
	booktitle    = {Case-{Based} {Reasoning}: {Papers} from the {Eighth} {German} {Workshop}. {L{\"a}mmerbuckel}, {Germany}: {DaimlerChrysler} {Research} and {Technology}},
	editor       = {G{\"o}ker, Mehmet}
}

@inproceedings{minor_life_2000,
	title        = {The {Life} {Cycle} of {Test} {Cases} in a {CBR} {System}},
	author       = {Minor, Mirjam and Hanft, Alexandre},
	year         = 2000,
	booktitle    = {Advances in {Case}-{Based} {Reasoning}: 5th {European} {Workshop}, {EWCBR} 2000},
	publisher    = {Springer},
	address      = {Berlin},
	series       = {{LNAI} 1898},
	pages        = {455 --466},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Blanzieri, Enrico and Portinale, Luigi}
}

@book{funk_proceedings_2000,
	title        = {Proceedings of the {ECAI}'2000 {Workshop} on {Flexible} {Strategies} for {Maintaining} {Knowledge} {Containers}},
	year         = 2000,
	publisher    = {Department of Computer Science of the Humboldt Universit{\"a}t zu Berlin},
	editor       = {Funk, Peter and Minor, Mirjam and Roth-Berghofer, Thomas and Wilson, David}
}

@inproceedings{stahl_applying_2000-1,
	title        = {Applying {Recursive} {CBR} for the {Custumization} of {Structured} {Products} in an {Electronic} {Shop}},
	author       = {Stahl, Armin and Bergmann, Ralph},
	year         = 2000,
	booktitle    = {Advances in {Case}-{Based} {Reasoning}, 5th {European} {Workshop}, {EWCBR} 2000, {Trento}, {Italy}, {September} 6-9, 2000, {Proceedings}},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Computer} {Science}},
	volume       = 1898,
	pages        = {297--308},
	isbn         = {3-540-67933-2},
	url          = {http://www.wi2.uni-trier.de/publications/2000_ewcbr_stahl_bergmann.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Blanzieri, Enrico and Portinale, Luigi}
}

@inproceedings{schumacher_efficient_2000,
	title        = {An {Efficient} {Approach} to {Similarity}-{Based} {Retrieval} on {Top} of {Relational} {Databases}},
	author       = {Schumacher, J{\"u}rgen and Bergmann, Ralph},
	year         = 2000,
	booktitle    = {Advances in {Case}-{Based} {Reasoning}, 5th {European} {Workshop}, {EWCBR} 2000, {Trento}, {Italy}, {September} 6-9, 2000, {Proceedings}},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Computer} {Science}},
	volume       = 1898,
	pages        = {273--284},
	isbn         = {3-540-67933-2},
	url          = {http://www.wi2.uni-trier.de/publications/2000_ewcbr_retrieval.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Blanzieri, Enrico and Portinale, Luigi}
}

@inproceedings{schmitt_product_2000,
	title        = {A {Product} {Customization} {Module} {Based} on {Adaptation} {Operators} for {CBR} {Systems} in {E}-{Commerce} {Environments}},
	author       = {Schmitt, Sascha and Maximini, Rainer and Landeck, Gerhard and Hohwiller, J{\"o}rg},
	year         = 2000,
	booktitle    = {Advances in {Case}-{Based} {Reasoning}, 5th {European} {Workshop}, {EWCBR} 2000, {Trento}, {Italy}, {September} 6-9, 2000, {Proceedings}},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Computer} {Science}},
	volume       = 1898,
	pages        = {504--516},
	isbn         = {3-540-67933-2},
	url          = {http://www.wi2.uni-trier.de/publications/2000_ewcbr.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Blanzieri, Enrico and Portinale, Luigi}
}

@inproceedings{weibelzahl_towards_2000,
	title        = {Towards an empirical evaluation of {CBR} approaches for product recommendeation in electronic shops},
	author       = {Weibelzahl, Stephan and Bergmann, Ralph and Weber, Gerhard},
	year         = 2000,
	booktitle    = {Case-{Based} {Reasoning}: {Papers} from the {Eighth} {German} {Workshop}. {L{\"a}mmerbuckel}, {Germany}: {DaimlerChrysler} {Research} and {Technology}},
	url          = {http://www.wi2.uni-trier.de/publications/2000_gwcbr_weibelzahl_bergmann.pdf},
	editor       = {G{\"o}ker, Mehmet}
}

@inproceedings{bergmann_generalized_1999,
	title        = {Generalized cases and their application to electronic designs},
	author       = {Bergmann, Ralph and Vollrath, Ivo and Wahlmann, T.},
	year         = 1999,
	booktitle    = {7th {German} {Workshop} on {Case}-{Based} {Reasoning}},
	url          = {http://www.wi2.uni-trier.de/publications/1999_GeneralizedCasesGWCBR.pdf},
	editor       = {Melis, E.}
}

@inproceedings{schmitt_product_1999,
	title        = {Product customization in an electronic commerce environment using adaptation operators},
	author       = {Schmitt, Sascha and Bergmann, Ralph},
	year         = 1999,
	booktitle    = {7th {German} {Workshop} on {Case}-{Based} {Reasoning}},
	url          = {http://www.wi2.uni-trier.de/publications/1999_schmittbergmann99.pdf},
	editor       = {Melis, E.}
}

@inproceedings{hullen_webplan:_1999,
	title        = {{WebPlan}: {Dynamic} planning for domain-specific search in the {Internet}},
	author       = {H{\"u}llen, Jochem and Bergmann, Ralph and Weberskirch, Frank},
	year         = 1999,
	booktitle    = {13. {Workshop} "{Planen} und {Konfigurieren}" ({PuK}-99)},
	url          = {http://www.wi2.uni-trier.de/publications/1999_PuKPaper.pdf},
	editor       = {K{\"o}hler, J.}
}

@inproceedings{schmitt_applying_1999,
	title        = {Applying case-based reasoning technology for product selection and customization in electronic commerce environments},
	author       = {Schmitt, Sascha and Bergmann, Ralph},
	year         = 1999,
	booktitle    = {12th {Bled} {Electronic} {Commerce} {Conference}},
	url          = {http://www.wi2.uni-trier.de/publications/1999_PaperBled.pdf}
}

@inproceedings{bergmann_developing_1999,
	title        = {Developing {Industrial} {Case}-{Based} {Reasoning} {Applications} {Using} the {INRECA} {Methodology}},
	author       = {Bergmann, Ralph and G{\"o}ker, Mehmet},
	year         = 1999,
	booktitle    = {{IJCAI}-99 {Workshop} on the automatic construction of case-based reasoners},
	url          = {http://www.wi2.uni-trier.de/publications/1999_ijcai.pdf},
	editor       = {Anand, S.S. and Aamodt, A. and Aha, D.}
}

@book{bergmann_engineering_1999,
	title        = {Engineering {Applications} of {Case}-{Based} {Reasoning}, {Special} {Issue} of the {International} {Journal} "{Engineering} {Applications} of {Artificial} {Intelligence}"},
	year         = 1999,
	publisher    = {Elsevier},
	volume       = 12,
	number       = 6,
	editor       = {Bergmann, Ralph}
}

@techreport{bergmann_design_1999,
	title        = {Design of the intelligent sales architecture, {WEBSELL} {Deliverables} {D}3.1 \& {D}4.1. {WEBSELL} {Consortium}},
	author       = {Bergmann, Ralph and Breen, Sean and Cunningham, P{\'a}draig and Dobal, R. and Grohmann, H. -J and McNaulty, P. and Schmitt, Sascha and Traph{\"o}ner, Ralph and Voltz, M. and Wolf, Max},
	year         = 1999,
	institution  = {University of Kaiserslautern}
}

@inproceedings{minor_managing_1999,
	title        = {Managing {Test} {Specifications} with {Case}-{Based} {Reasoning}},
	author       = {Minor, Mirjam},
	year         = 1999,
	booktitle    = {7th {German} {Workshop} on {CBR}},
	publisher    = {Universit{\"a}t Saarbr{\"u}cken},
	editor       = {Melis, Erica}
}

@inproceedings{minor_cases_1999,
	title        = {Cases {With} a {Life}-{Cycle}},
	author       = {Minor, Mirjam and Hanft, Alexandre},
	year         = 1999,
	booktitle    = {Challenges for {Case}-{Based} {Reasoning} - {Proceedings} of the {ICCBR}'99 {Workshops}},
	publisher    = {Universit{\"a}t Kaiserslautern},
	editor       = {Schmitt, Sascha and Vollrath, Ivo}
}

@inproceedings{minor_semi-automatic_1999,
	title        = {Semi-{Automatic} {Knowledge} {Acquisition} for {Textual} {Case}-{Based} {Reasoning}},
	author       = {Minor, Mirjam and H{\"u}bner, Andre},
	year         = 1999,
	booktitle    = {{IJCAI}-99 {Workshop} {Text} {Mining}: {Foundations}, {Techniques} and {Applications}},
	publisher    = {Bar-Ilan University, Ramat-Gan, Israel},
	editor       = {Feldman, Ronan and Hirsh, Haym}
}

@book{bergmann_developing_1999-1,
	title        = {Developing {Industrial} {Case}-{Based} {Reasoning} {Applications}: {The} {INRECA} {Methodology}.},
	author       = {Bergmann, Ralph and Breen, Sean and G{\"o}ker, Mehmet and Manago, Michel and Wess, Stefan},
	year         = 1999,
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	volume       = 1612,
	url          = {http://www.springer.com/computer/ai/book/978-3-540-20737-5},
	note         = {The original publication is available at www.springerlink.com}
}

@book{althoff_case-based_1999,
	title        = {Case-{Based} {Reasoning} {Research} and {Development}, {Third} {International} {Conference} on {Case}-{Based} {Reasoning}, {ICCBR}-99},
	year         = 1999,
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	volume       = 1650,
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Althoff, Klaus-Dieter and Bergmann, Ralph and Branting, Karl}
}

@inproceedings{bergmann_generalized_1999-1,
	title        = {Generalized {Cases}: {Representation} and {Steps} {Towards} {Efficient} {Similarity} {Assessment}},
	author       = {Bergmann, Ralph and Vollrath, Ivo},
	year         = 1999,
	booktitle    = {Proceedings of the 23rd {Annual} {German} {Conference} on {Artificial} {Intelligence}: {Advances} in {Artificial} {Intelligence}},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	volume       = 1701,
	pages        = {195--206},
	url          = {http://www.wi2.uni-trier.de/publications/1999_ki.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Burgard, W. and Christaller, Th. and Cremers, A.B.}
}

@inproceedings{koegst_ip_1999,
	title        = {{IP} {Retrieval} by solving constraint satisfaction problems},
	author       = {Koegst, M. and Schneider, J. and Bergmann, Ralph and Vollrath, Ivo},
	year         = 1999,
	booktitle    = {{FDL} '99, {Second} {International} {Forum} on {Design} {Languages}},
	url          = {http://www.wi2.uni-trier.de/publications/1999_Koegst_Lyon.pdf}
}

@article{bergmann_preface_1999,
	title        = {Preface to the special issue on {Engineering} {Applications} of {Case}-{Based} {Reasoning}},
	author       = {Bergmann, Ralph},
	year         = 1999,
	journal      = {Engineering Applications of Artificial Intelligence},
	volume       = 12,
	number       = 6,
	pages        = {661--663},
	url          = {http://www.wi2.uni-trier.de/publications/1999_Preface_EAAI.pdf},
	note         = {Elsevier}
}

@inproceedings{bendeck_coordinating_1998,
	title        = {Coordinating {Management} {Activities} in {Distributed} {Software} {Development} {Projects}},
	author       = {Bendeck, Fawsy and Goldmann, Sigrid and Holz, Harald and K{\"o}tting, Boris},
	year         = 1998,
	month        = jun,
	booktitle    = {7 th {IEEE} {International} {Workshops} on {Enabling} {Technologies}: {Infrastructure} for {Collaborative} {Enterprises} ({WET} {ICE} '98)},
	publisher    = {IEEE Computer Society Press},
	address      = {Stanford University, CA},
	pages        = {33--38}
}

@inproceedings{bergmann_efficient_1998,
	title        = {Efficient {Retrieval} of {Abstract} {Cases} for {Case}-{Based} {Planning}},
	author       = {Bergmann, Ralph},
	year         = 1998,
	booktitle    = {Proceedings of the {Symposium} on {Abstraction}, {Reformulation} and {Approximation} ({SARA}'98)},
	publisher    = {Rutgers University},
	pages        = {9--18},
	url          = {http://www.wi2.uni-trier.de/publications/1998_sara_bergmann.pdf}
}

@inproceedings{wilke_negotiation_1998,
	title        = {Negotiation {During} {Intelligent} {Sales} {Support} with {Case}-{Based} {Reasoning}},
	author       = {Wilke, Wolfgang and Bergmann, Ralph and Wess, Stefan},
	year         = 1998,
	booktitle    = {Proceedings of the 6th {German} {Workshop} on {Case}-{Based} {Reasoning} ({GWCBR}'98)},
	url          = {http://www.wi2.uni-trier.de/publications/1998_Negotiation_wilke_atall.pdf},
	editor       = {Gierl, Lothar and Lenz, Mario}
}

@inproceedings{bergmann_inreca-ii_1998,
	title        = {The {INRECA}-{II} {Methodology} for {Building} and {Maintaining} {CBR} {Applications}},
	author       = {Bergmann, Ralph and Breen, Sean and G{\"o}ker, Mehmet and Manago, Michel and Schmitt, Sascha and Schumacher, J{\"u}rgen and Stahl, Armin and Tartarin, Emmanuelle and Wess, Stefan and Wilke, Wolfgang},
	year         = 1998,
	booktitle    = {Proceedings of the 6th {German} {Workshop} on {Case}-{Based} {Reasoning} ({GWCBR}'98)},
	url          = {http://www.wi2.uni-trier.de/publications/1998_INRECA GWCBR.pdf},
	editor       = {Gierl, Lothar and Lenz, Mario}
}

@inproceedings{bergmann_towards_1998,
	title        = {Towards a {New} {Formal} {Model} of {Transformational} {Adaptation} in {Case}-{Based} {Reasoning}},
	author       = {Bergmann, Ralph and Wilke, Wolfgang},
	year         = 1998,
	booktitle    = {13th {European} {Conference} on {Artificial} {Intelligence} ({ECAI}'98)},
	publisher    = {John Wiley \& Sons},
	pages        = {53--57},
	url          = {http://www.wi2.uni-trier.de/publications/1998_ecai.pdf},
	editor       = {Prade, Henry}
}

@inproceedings{bergmann_use_1998,
	title        = {On the {Use} of {Taxonomies} for {Representing} {Case} {Features} and {Local} {Similarity} {Measures}},
	author       = {Bergmann, Ralph},
	year         = 1998,
	booktitle    = {Proceedings of the 6th {German} {Workshop} on {Case}-{Based} {Reasoning} ({GWCBR}'98)},
	url          = {http://www.wi2.uni-trier.de/publications/1998_BergmannGwcbr.pdf},
	editor       = {Gierl, Lothar and Lenz, Mario}
}

@article{althoff_case-based_1998,
	title        = {Case-{Based} {Reasoning} for {Medical} {Decision} {Support} {Tasks}: {The} {INRECA} {Approach}},
	author       = {Althoff, Klaus-Dieter and Bergmann, Ralph and Wess, Stefan and Manago, Michel and Auriol, Eric and Larichev, O. and Bolotov, Alexander and Zhuravlev, Yurii I and Gurov, Serge I},
	year         = 1998,
	journal      = {Artificial Intelligence in Medicine Journal (AIMJ)},
	volume       = 12,
	number       = 1,
	pages        = {25--41},
	url          = {http://www.wi2.uni-trier.de/publications/1998_AI_in_med.pdf}
}

@inproceedings{kunze_cbr_1998,
	title        = {{CBR} on {Semi}-structured {Documents}: {The} {ExperienceBook} and the {FAllQ} {Project}},
	author       = {Kunze, Mirjam and H{\"u}bner, Andre},
	year         = 1998,
	booktitle    = {6th {German} {Workshop} on {CBR}},
	publisher    = {Universit{\"a}t Rostock},
	address      = {Rostock},
	pages        = {77--85},
	note         = {IMIB Series Vol. 7},
	editor       = {Gierl, Lothar and Lenz, Mario}
}

@techreport{manago_specification_1998,
	title        = {Specification of tools that support the methodology. {INRECA}-{II} {Deliverable} {D}7, {INRECA} {Consortium}},
	author       = {Manago, Michel and Bergmann, Ralph and Stahl, Armin and Wilke, Wolfgang},
	year         = 1998,
	institution  = {University of Kaiserslautern}
}

@techreport{bergmann_refined_1998,
	title        = {Refined methodology for building and maintaining a {CBR} application. {ESPRIT}-{Project} {INRECA}-{II}, {Deliverable} {D}6, {INRECA} {Consortium}},
	author       = {Bergmann, Ralph and Breen, Sean and Fayol, E. and G{\"o}ker, Mehmet and Johnston, Roy and Schmitt, Sascha and Schumacher, J{\"u}rgen and Stahl, Armin and Traph{\"o}ner, Ralph and Wess, Stefan and Wilke, Wolfgang},
	year         = 1998,
	institution  = {University of Kaiserslautern}
}

@inproceedings{lenz_textual_1998,
	title        = {Textual {CBR}},
	author       = {Lenz, Mario and H{\"u}bner, Andre and Kunze, Mirjam},
	year         = 1998,
	booktitle    = {Case-{Based} {Reasoning} {Technology} {\textemdash} {From} {Foundations} to {Applications}},
	publisher    = {Springer},
	address      = {Berlin},
	series       = {{LNAI} 1400},
	pages        = {115--137},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Lenz, Mario and Bartsch-Sp{\"o}rl, Brigitte and Burkhard, Hans-Dieter and Wess, Stefan}
}

@inproceedings{wilke_techniques_1998,
	title        = {Techniques and knowledge used for adaptation during base-cased problem solving},
	author       = {Wilke, Wolfgang and Bergmann, Ralph},
	year         = 1998,
	booktitle    = {Tasks and {Methods} in {Applied} {Artificial} {Intelligence}, 11th {International} {Conference} on {Industrial} and {Engineering} {Applications} of {Artificial} {In} telligence and {Expert} {Systems}, {IEA}/{AIE}-98, {Castell{\'o}n}, {Spain}, {June} 1-4, 1998, {Proceedings}, {Volume} {II}},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Computer} {Science}},
	volume       = 1416,
	pages        = {497--506},
	isbn         = {3-540-64574-8},
	url          = {http://www.wi2.uni-trier.de/publications/1998_wilketechniques.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Pobil, Angel P. Del and Mira, Jos{\'e} and Ali, Moonis}
}

@inproceedings{vollrath_intelligent_1998,
	title        = {Intelligent {Electronic} {Catalogs} for {Sales} {Support} - {Introducing} {Case}-{Based} {Reasoning} {Techniques} to {On}-{Line} {Product} {Selection} {Applications}},
	author       = {Vollrath, Ivo and Wilke, Wolfgang and Bergmann, Ralph},
	year         = 1998,
	booktitle    = {Proceedings of the 3rd {World} {Conference} on {Soft} {Computing}: {Advances} in {Soft} {Computing} - {Engineering} {Design} and {Manufacturing}},
	address      = {Springer},
	url          = {http://www.wi2.uni-trier.de/publications/1998_Catalogs.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Roy, R. and Furuhashi, T. and Chawddhry, P.K.}
}

@inproceedings{lenz_question_1998,
	title        = {Question {Answering} with {Textual} {CBR}},
	author       = {Lenz, Mario and H{\"u}bner, Andre and Kunze, Mirjam},
	year         = 1998,
	booktitle    = {Flexible {Query} {Answering} {Systems}},
	publisher    = {Springer-Verlag},
	address      = {Berlin},
	series       = {{LNAI} 1495},
	pages        = {236--247},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Andreasen, Troels and Christiansen, Henning and Larsen, Henrik Legind}
}

@inproceedings{goker_development_1998,
	title        = {The {Development} of {HOMER}: {A} {Case}-{Based} {CAD}/{CAM} {Help}-{Desk} {Support} {Tool}},
	author       = {G{\"o}ker, Mehmet H. and Roth-Berghofer, Thomas and Bergmann, Ralph and Pantleon, Thomas and Traph{\"o}ner, Ralph and Wess, Stefan and Wilke, Wolfgang},
	year         = 1998,
	booktitle    = {Advances in {Case}-{Based} {Reasoning}, 4th {European} {Workshop}, {EWCBR}-98, {Dublin}, {Ireland}, {September} 1998, {Proceedings}},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Computer} {Science}},
	volume       = 1488,
	pages        = {346--357},
	isbn         = {3-540-64990-5},
	url          = {http://www.wi2.uni-trier.de/publications/1998_homer_ewcbr.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Smyth, Barry and Cunningham, Padraig}
}

@inproceedings{bergmann_similarity_1998,
	title        = {Similarity {Measures} for {Object}-{Oriented} {Case} {Representations}},
	author       = {Bergmann, Ralph and Stahl, Armin},
	year         = 1998,
	booktitle    = {Advances in {Case}-{Based} {Reasoning}, 4th {European} {Workshop}, {EWCBR}-98, {Dublin}, {Ireland}, {September} 1998, {Proceedings}},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Computer} {Science}},
	volume       = 1488,
	pages        = {25--36},
	isbn         = {3-540-64990-5},
	url          = {http://www.wi2.uni-trier.de/publications/1998_EWCBR98_similarity_CRC.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Smyth, Barry and Cunningham, Padraig}
}

@inproceedings{bergmann_collecting_1998,
	title        = {Collecting {Experience} on the {Systematic} {Development} of {CBR} {Applications} {Using} the {INRECA} {Methodology}},
	author       = {Bergmann, Ralph and Breen, Sean and Fayol, Emmanuelle and G{\"o}ker, Mehmet H. and Manago, Michel and Schmitt, Sascha and Schumacher, J{\"u}rgen and Stahl, Armin and Wess, Stefan and Wilke, Wolfgang},
	year         = 1998,
	booktitle    = {Advances in {Case}-{Based} {Reasoning}, 4th {European} {Workshop}, {EWCBR}-98, {Dublin}, {Ireland}, {September} 1998, {Proceedings}},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Computer} {Science}},
	volume       = 1488,
	pages        = {460--470},
	isbn         = {3-540-64990-5},
	url          = {http://www.wi2.uni-trier.de/publications/1998_bergmann_ewcbr_collecting.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Smyth, Barry and Cunningham, Padraig}
}

@techreport{kunze_experiencebook_1998,
	title        = {Das {ExperienceBook} - {Dokumentation} eines fallbasierten {Systems} zur {Unterst{\"u}tzung} der {Systemadministration}},
	author       = {Kunze, Mirjam},
	year         = 1998,
	institution  = {Diplomarbeit, Humboldt-Universit{\"a}t zu Berlin}
}

@inproceedings{bergmann_towards_1998-1,
	title        = {Towards a {New} {Formal} {Model} of {Transformational} {Adaptation} in {Case}-{Based} {Reasoning}},
	author       = {Bergmann, Ralph and Wilke, Wolfgang},
	year         = 1998,
	booktitle    = {Proceedings of the 6th {German} {Workshop} on {Case}-{Based} {Reasoning} ({GWCBR}'98)}
}

@inproceedings{minor_managing_1998,
	title        = {Managing {Test} {Specifications} with {Case}-{Based} {Reasoning}},
	author       = {Minor, Mirjam},
	year         = 1998,
	booktitle    = {Proceedings of the {Workshop} on {Concurrency}, {Specification} and {Programming} ({CS}\&{P}-98)},
	publisher    = {Humboldt-Universit{\"a}t zu Berlin},
	address      = {Berlin},
	editor       = {Burkhard, Hans-Dieter and Czaja, Ludwik and Starke, Peter}
}

@article{vollrath_case-based_1998,
	title        = {Case-based reasoning support for online catalog sales},
	author       = {Vollrath, I. and Wilke, W. and Bergmann, R.},
	year         = 1998,
	month        = aug,
	journal      = {Internet Computing, IEEE},
	volume       = 2,
	number       = 4,
	pages        = {47 --54},
	doi          = {10.1109/4236.707690},
	issn         = {1089-7801},
	url          = {http://www.wi2.uni-trier.de/publications/1998_w4voll.pdf},
	keywords     = {based, catalog, catalogs;electronic, commerce;information, data, Internet;case-based, knowledge;electronic, measures;Internet;case-based, processing;, reasoning;databases;domain-specific, reasoning;knowledge, retrieval;intelligent, sales, sales;problem, solving;similarity, support;online, systems;marketing}
}

@inproceedings{carranza_proposal_1998,
	title        = {Proposal for a planning approach for information seeking},
	author       = {Carranza, C. and Munoz-Avila, H. and Weberskirch, F. and Bergmann, R.},
	year         = 1998,
	booktitle    = {{AIPS}'98 {Workshop} on {Integrating} {Planning}, {Scheduling} and {Execution} in {Dynamic} and {Uncertain} {Environments}},
	publisher    = {AAAI Press},
	volume       = 98,
	url          = {http://www.wi2.uni-trier.de/publications/1998_Carranza_etal.pdf},
	editor       = {Bergmann, Ralph and Kott, Alexander}
}

@inproceedings{oehler_are_1998,
	title        = {Are your {READee} for {IPs}?},
	author       = {Oehler, Peter and Vollrath, Ivo and Conradi, Peter and Bergmann, Ralph},
	year         = 1998,
	booktitle    = {Second {GI}/{ITG}/{GMM} {Workshop} "{Reuse} {Techniques} for {VLSI} {Design}},
	publisher    = {Universit{\"a}t Karlsruhe},
	series       = {{FZI}-{Report}},
	url          = {http://www.wi2.uni-trier.de/publications/1998_Readee.pdf},
	editor       = {Kunzmann, A. and Seepold, R.}
}

@book{bergmann_integrating_1998,
	title        = {Integrating {Planning}, {Scheduling} and {Execution} in {Dynamic} and {Uncertain} {Environments}},
	year         = 1998,
	publisher    = {AAAI Technical Report WS-98-02, AAAI Press},
	url          = {http://www.aaai.org/Library/Workshops/ws98-02.php},
	editor       = {Bergmann, Ralph and Kott, W.}
}

@inproceedings{oehler_readee_1998,
	title        = {{READee} - {Decision} {Support} for {IP} {Selection} using a {Knowledge}-{Based} {Approach}},
	author       = {Oehler, Peter and Vollrath, Ivo and Conradi, Peter and Bergmann, Ralph and Wahlmann, Thomas},
	year         = 1998,
	booktitle    = {In {IP}98 {Europe} {Proceedings}},
	publisher    = {Miller Freeman},
	url          = {http://www.wi2.uni-trier.de/publications/1998_IP98.pdf}
}

@incollection{bergmann_case-based_1998,
	title        = {Case-based reasoning applied to planning tasks},
	author       = {Bergmann, Ralph and Munoz-Avila, Hector and Veloso, M. and Melis, E.},
	year         = 1998,
	booktitle    = {Case-{Based} {Reasoning} {Technology} from {Foundations} to {Applications}},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	number       = 1400,
	pages        = {169--199},
	url          = {http://www.wi2.uni-trier.de/publications/1998_CBRBuch_Chapter7.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Lenz, Mario and Bartsch-Sp{\"o}rl, Brigitte and Burkhard, H. -D and Wess, Stefan}
}

@incollection{bergmann_case-based_1998-1,
	title        = {Case-based reasoning applied to planning tasks},
	author       = {Bergmann, Ralph and Althoff, Klaus-Dieter},
	year         = 1998,
	booktitle    = {Case-{Based} {Reasoning} {Technology} from {Foundations} to {Applications}},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	number       = 1400,
	pages        = {299--326},
	url          = {http://www.wi2.uni-trier.de/publications/1998_CBRBuch_Chapter7.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Lenz, Mario and Bartsch-Sp{\"o}rl, Brigitte and Burkhard, H. -D and Wess, Stefan}
}

@inproceedings{wilke_framework_1997,
	title        = {A framework for learning adaptation knowledge based on knowledge light approaches},
	author       = {Wilke, W. and Vollrath, I. and Althoff, K. D and Bergmann, R.},
	year         = 1997,
	booktitle    = {Proceedings of the {Fifth} {German} {Workshop} on {Case}-{Based} {Reasoning}},
	pages        = {235--242},
	url          = {http://www.wi2.uni-trier.de/publications/1997_GWCBR_WilkeEtAl.pdf}
}

@inproceedings{bergmann_ingredients_1997,
	title        = {Ingredients for {Developing} a {Case}-{Based} {Reasoning} {Methodology}},
	author       = {Bergmann, Ralph and Wilke, Wolfgang and Althoff, Klaus-Dieter and Breen, Sean and Johnston, Roy},
	year         = 1997,
	booktitle    = {Proceedings of the {Fifth} {German} {Workshop} on {Case}-{Based} {Reasoning}},
	publisher    = {Universit{\"a}t Kaiserslautern},
	pages        = {49--58},
	url          = {http://www.wi2.uni-trier.de/publications/1997_INRECA GWCBR.pdf}
}

@inproceedings{wilke_using_1997,
	title        = {Using {Knowledge} {Containers} to {Model} a {Framework} for {Learning} {Adaptation} {Knowledge}},
	author       = {Wilke, Wolfgang and Vollrath, Ivo and Bergmann, Ralph},
	year         = 1997,
	booktitle    = {European {Conference} on {Machine} {Learning}. {MLNet} {Workshop} {Notes} - {Case}-{Based} {Learning}: {Beyond} {Classification} of {Feature} {Vectors}},
	publisher    = {Navy Center for Applied Research in Artificial Intelligence},
	address      = {Naval Research Laboratory, Washington, D. C., USA},
	pages        = {68--75},
	url          = {http://www.wi2.uni-trier.de/publications/1997_WilkeVollrathBergmannECML.pdf},
	abstract     = {In this paper, we present a framework for learning adaptation knowledge which we call knowledge light approaches for case-based reasoning (CBR) systems. Knowledge light means that these approaches use already acquired knowledge inside the CBR system. Therefore, we describe the sources of knowledge inside a CBR system along the different knowledge containers. After that we present our framework in terms of these knowledge containers. Further, we apply our framework in a case study to one knowledge light approach for learning adaptation knowledge. After that we point on some issues which should be addressed during the design or the use of such algorithms for learning adaptation knowledge. From our point of view many of these issues should be the topic of further research. Finally, we close with a short discussion and an outlook to further work.},
	editor       = {Wettschereck, Dietrich and Aha, David W.},
	keywords     = {Adaptation, CBR, Container, Knowledge}
}

In this paper, we present a framework for learning adaptation knowledge which we call knowledge light approaches for case-based reasoning (CBR) systems. Knowledge light means that these approaches use already acquired knowledge inside the CBR system. Therefore, we describe the sources of knowledge inside a CBR system along the different knowledge containers. After that we present our framework in terms of these knowledge containers. Further, we apply our framework in a case study to one knowledge light approach for learning adaptation knowledge. After that we point on some issues which should be addressed during the design or the use of such algorithms for learning adaptation knowledge. From our point of view many of these issues should be the topic of further research. Finally, we close with a short discussion and an outlook to further work.

@inproceedings{bergmann_using_1997,
	title        = {Using {Software} {Process} {Modeling} for {Building} a {Case}-{Based} {Reasoning} {Methodology}: {Basis} {Approach} and {Case} {Study}},
	author       = {Bergmann, Ralph and Wilke, Wolfgang and Schumacher, J{\"u}rgen},
	year         = 1997,
	booktitle    = {Case-{Based} {Reasoning} {Research} and {Development}, {Second} {International} {Conference}, {ICCBR}-97, {Providence}, {Rhode} {Island}, {USA}, {July} 25-27, 1997, {Proceedings}},
	publisher    = {Springer},
	series       = {Lecture {Notes} in {Artificial} {Intelligence}},
	volume       = 1266,
	pages        = {509--518},
	isbn         = {3-540-63233-6},
	url          = {http://www.wi2.uni-trier.de/publications/1997_BergmannIccbr.pdf},
	note         = {The original publication is available at www.springerlink.com},
	editor       = {Leake, David B. and Plaza, Enric}
}

@inproceedings{bergmann_integrating_1996,
	title        = {Integrating {General} {Knowledge} with {Object}-{Oriented} {Case} {Representation} and {Reasoning}},
	author       = {Bergmann, Ralph and Wilke, Wolfgang and Vollrath, Ivo and Wess, Stefan},
	year         = 1996,
	booktitle    = {4th {German} {Workshop}: {Case}-{Based} {Reasoning} - {System} {Development} and {Evaluation}},
	publisher    = {Humboldt-Universit{\"a}t Berlin},
	address      = {Informatik-Berichte Nr. 55},
	pages        = {120--126},
	url          = {http://www.wi2.uni-trier.de/publications/1996_FGCBR.pdf},
	editor       = {Burkhard, Hans-Dieter and Wess, Stefan}
}