@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), Merida,
                  Mexico, July 1, 2024},
  series       = {{CEUR} Workshop Proceedings},
  publisher    = {CEUR-WS.org.},
  note		   = {{Accepted for Publication.}},
  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}}
}

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},
  publisher    = {Springer.},
  note		   = {{Accepted for Publication.}},
  year         = {2024},
  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}}
}

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{Lenz.2024_CBRkit,
  author       = {Lenz, Mirko and Malburg, Lukas and Bergmann, Ralph},
  title        = {{CBRkit: An Intuitive Case-Based Reasoning Toolkit for Python}},
  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},
  publisher    = {Springer.},
  note		   = {{Accepted for Publication.}},
  year         = {2024},
  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.}},
  keywords 	   = {{Case-Based Reasoning, Machine Learning, Hybrid AI, CBR Frameworks, Python Library}}
}

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{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},
  publisher    = {Springer.},
  note		   = {{Accepted for Publication.}},
  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}}
}

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.

@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{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},
  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_Malburg_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.

@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{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},
	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.

@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_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{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.

@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{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.

@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.

@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{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{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.

@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{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{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.