Improving Automated Hyperparameter Optimization with Case-Based Reasoning.
Hoffmann, M.; and Bergmann, R.
In Keane, M. T.; and Wiratunga, N., editor(s),
Case-Based Reasoning Research and Development - 30th International Conference, ICCBR 2022, Nancy, France, September 12-15, 2022, Proceedings, volume 13405, of
Lecture Notes in Computer Science, pages 273–288, 2022. Springer
Paper
doi
link
bibtex
abstract
21 downloads
@inproceedings{hoffmann_hyperparameters_2022,
author = {Maximilian Hoffmann and
Ralph Bergmann},
editor = {Mark T. Keane and
Nirmalie Wiratunga},
title = {{Improving Automated Hyperparameter Optimization with Case-Based Reasoning}},
booktitle = {Case-Based Reasoning Research and Development - 30th International
Conference, {ICCBR} 2022, Nancy, France, September 12-15, 2022, Proceedings},
series = {Lecture Notes in Computer Science},
volume = {13405},
pages = {273--288},
publisher = {Springer},
year = {2022},
abstract = {The hyperparameter configuration of machine learning models has a great influence on their performance. These hyperparameters are often set either manually w. r. t. to the experience of an expert or by an Automated Hyperparameter Optimization (HPO) method. However, integrating experience knowledge into HPO methods is challenging. Therefore, we propose the approach HypOCBR (Hyperparameter Optimization with Case-Based Reasoning) that uses Case-Based Reasoning (CBR) to improve the optimization of hyperparameters. HypOCBR is used as an addition to HPO methods and builds up a case base of sampled hyperparameter vectors with their loss values. The case base is then used to retrieve hyperparameter vectors given a query vector and to make decisions whether to proceed trialing with this query or abort and sample another vector. The experimental evaluation investigates the suitability of HypOCBR for two deep learning setups of varying complexity. It shows its potential to improve the optimization results, especially in complex scenarios with limited optimization time.},
url = {http://www.wi2.uni-trier.de/shared/publications/2022_ICCBR__Hyperparameter_Optimization_with_CBR.pdf},
doi = {10.1007/978-3-031-14923-8\_18}
}
The hyperparameter configuration of machine learning models has a great influence on their performance. These hyperparameters are often set either manually w. r. t. to the experience of an expert or by an Automated Hyperparameter Optimization (HPO) method. However, integrating experience knowledge into HPO methods is challenging. Therefore, we propose the approach HypOCBR (Hyperparameter Optimization with Case-Based Reasoning) that uses Case-Based Reasoning (CBR) to improve the optimization of hyperparameters. HypOCBR is used as an addition to HPO methods and builds up a case base of sampled hyperparameter vectors with their loss values. The case base is then used to retrieve hyperparameter vectors given a query vector and to make decisions whether to proceed trialing with this query or abort and sample another vector. The experimental evaluation investigates the suitability of HypOCBR for two deep learning setups of varying complexity. It shows its potential to improve the optimization results, especially in complex scenarios with limited optimization time.
GPU-Based Graph Matching for Accelerating Similarity Assessment in Process-Oriented Case-Based Reasoning.
Hoffmann, M.; Malburg, L.; Bach, N.; and Bergmann, R.
In Keane, M. T.; and Wiratunga, N., editor(s),
Case-Based Reasoning Research and Development - 30th International Conference, ICCBR 2022, Nancy, France, September 12-15, 2022, Proceedings, volume 13405, of
Lecture Notes in Computer Science, pages 240–255, 2022. Springer
Paper
doi
link
bibtex
abstract
11 downloads
@inproceedings{hoffmann_gpu_astar_2022,
author = {Maximilian Hoffmann and Lukas Malburg and Nico Bach and
Ralph Bergmann},
editor = {Mark T. Keane and
Nirmalie Wiratunga},
title = {{GPU-Based Graph Matching for Accelerating Similarity Assessment in Process-Oriented Case-Based Reasoning}},
booktitle = {Case-Based Reasoning Research and Development - 30th International Conference, {ICCBR} 2022, Nancy, France, September 12-15, 2022, Proceedings},
series = {Lecture Notes in Computer Science},
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.},
volume = {13405},
pages = {240--255},
publisher = {Springer},
doi = {10.1007/978-3-031-14923-8\_16},
url = {http://www.wi2.uni-trier.de/shared/publications/2022_ICCBR__A_Star_GPU.pdf},
year = {2022}
}
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.
Towards Adaptive Workflow Management by Case-Based Reasoning and Automated Planning.
Malburg, L.; and Bergmann, R.
In Reuss, P.; and Schönborn, J., editor(s),
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, of
CEUR Workshop Proceedings, 2022. CEUR-WS.org
Accepted for Publication.
link
bibtex
abstract
9 downloads
@inproceedings{Malburg_AdaptiveWorkflowManagement_2022,
title = {{Towards Adaptive Workflow Management by Case-Based Reasoning and Automated Planning}},
author = {Malburg, Lukas and Bergmann, Ralph},
year = {2022},
editor = {Pascal Reuss and Jakob Schönborn},
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}},
series = {{CEUR} Workshop Proceedings},
publisher = {CEUR-WS.org},
note = {Accepted for Publication.},
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.
Towards Experience-based Assistance for Personal Robotic Process Automation by Process-Oriented Case-based Reasoning.
Zeyen, C.; Koch, R.; Schwarz, S.; Maus, H.; and Bergmann, R.
In Reuss, P.; and Schönborn, J., editor(s),
ICCBR POCBR’22: Workshop on Process-Oriented Case-based Reasoning at ICCBR-2022, September 2022, Nancy, France, of
CEUR Workshop Proceedings, 2022. CEUR-WS.org
Paper
link
bibtex
11 downloads
@inproceedings{Zeyen_myRPA_2022,
title = {{Towards Experience-based Assistance for Personal Robotic Process Automation by Process-Oriented Case-based Reasoning}},
author = {Zeyen, Christian and Koch, Rudolf, and Schwarz, Sven and Maus, Heiko and Bergmann, Ralph},
year = {2022},
editor = {Pascal Reuss and Jakob Schönborn},
booktitle = {{ICCBR POCBR’22: Workshop on Process-Oriented Case-based Reasoning at ICCBR-2022, September 2022, Nancy, France}},
series = {{CEUR} Workshop Proceedings},
publisher = {CEUR-WS.org},
url = {http://www.wi2.uni-trier.de/shared/publications/2022_ZeyenEtal_myRPA_ICCBR.pdf}
}
FlexiTeam: Flexible Team andWork Organization using Process-Oriented Case-Based Reasoning.
Mathew, D.; Bergmann, R.; Weyers, B.; Ellwart, T.; Bohrmann, D.; and Hölzchen, E.
In Reuss, P.; and Schönborn, J., editor(s),
ICCBR POCBR’22: Workshop on Process-Oriented Case-based Reasoning at ICCBR-2022, September 2022, Nancy, France, of
CEUR Workshop Proceedings, 2022. CEUR-WS.org
Paper
link
bibtex
1 download
@inproceedings{Mathew_FlexiTeams_2022,
title = {{FlexiTeam: Flexible Team andWork Organization using Process-Oriented Case-Based Reasoning}},
author = {Mathew, Ditty and Bergmann, Ralph and Weyers, Benjamin and Ellwart, Thomas and Bohrmann, Dominique and Hölzchen, Ericson},
year = {2022},
editor = {Pascal Reuss and Jakob Schönborn},
booktitle = {{ICCBR POCBR’22: Workshop on Process-Oriented Case-based Reasoning at ICCBR-2022, September 2022, Nancy, France}},
series = {{CEUR} Workshop Proceedings},
publisher = {CEUR-WS.org},
url = {http://www.wi2.uni-trier.de/shared/publications/2022_Mathewetal_ICCBR.pdf}
}
ProGAN: Toward a Framework for Process Monitoring and Flexibility by Change via Generative Adversarial Networks.
Hoffmann, M.; Malburg, L.; and Bergmann, R.
In Marrella, A.; and Weber, B., editor(s),
Business Process Management Workshops - BPM 2021, Rome, Italy, September 6 - 10, 2021, volume 436, of
Lecture Notes in Business Information Processing, pages 43–55, 2022. Springer.
The original publication is available at www.springerlink.com
Paper
doi
link
bibtex
abstract
18 downloads
@inproceedings{Hoffmann.2022_ProGAN,
author = {Maximilian Hoffmann and
Lukas Malburg and
Ralph Bergmann},
editor = {Andrea Marrella and Barbara Weber},
title = {{ProGAN: Toward a Framework for Process Monitoring and Flexibility by Change via Generative Adversarial Networks}},
booktitle = {Business Process Management Workshops - {BPM} 2021,
Rome, Italy, September 6 - 10, 2021},
series = {Lecture Notes in Business Information Processing},
volume = {436},
pages = {43--55},
publisher = {Springer.},
keywords = {{Business process prediction, Generative Adversarial Networks, Flexibility by change, Process adaptation}},
year = {2022},
url = {https://doi.org/10.1007/978-3-030-94343-1\_4},
doi = {10.1007/978-3-030-94343-1\_4},
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.},
url = {http://www.wi2.uni-trier.de/shared/publications/2022_AI4BPM_HoffmannEtal_ProGAN.pdf},
note = {The original publication is available at www.springerlink.com}
}
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.
Using Graph Embedding Techniques in Process-Oriented Case-Based Reasoning.
Hoffmann, M.; and Bergmann, R.
Algorithms, 15(2). 2022.
Paper
doi
link
bibtex
abstract
18 downloads
@Article{Hoffmann.2022_GraphEmbeddingPOCBR,
AUTHOR = {Maximilian Hoffmann and Ralph Bergmann},
TITLE = {{Using Graph Embedding Techniques in Process-Oriented Case-Based Reasoning}},
JOURNAL = {Algorithms},
VOLUME = {15},
YEAR = {2022},
NUMBER = {2},
ARTICLE-NUMBER = {27},
URL = {https://www.mdpi.com/1999-4893/15/2/27/pdf},
ISSN = {1999-4893},
ABSTRACT = {Similarity-based retrieval of semantic graphs is a core task of Process-Oriented Case-Based Reasoning (POCBR) with applications in real-world scenarios, e.g., in smart manufacturing. The involved similarity computation is usually complex and time-consuming, as it requires some kind of inexact graph matching. To tackle these problems, we present an approach to modeling similarity measures based on embedding semantic graphs via Graph Neural Networks (GNNs). Therefore, we first examine how arbitrary semantic graphs, including node and edge types and their knowledge-rich semantic annotations, can be encoded in a numeric format that is usable by GNNs. Given this, the architecture of two generic graph embedding models from the literature is adapted to enable their usage as a similarity measure for similarity-based retrieval. Thereby, one of the two models is more optimized towards fast similarity prediction, while the other model is optimized towards knowledge-intensive, more expressive predictions. The evaluation examines the quality and performance of these models in preselecting retrieval candidates and in approximating the ground-truth similarities of a graph-matching-based similarity measure for two semantic graph domains. The results show the great potential of the approach for use in a retrieval scenario, either as a preselection model or as an approximation of a graph similarity measure.},
DOI = {10.3390/a15020027}
}
Similarity-based retrieval of semantic graphs is a core task of Process-Oriented Case-Based Reasoning (POCBR) with applications in real-world scenarios, e.g., in smart manufacturing. The involved similarity computation is usually complex and time-consuming, as it requires some kind of inexact graph matching. To tackle these problems, we present an approach to modeling similarity measures based on embedding semantic graphs via Graph Neural Networks (GNNs). Therefore, we first examine how arbitrary semantic graphs, including node and edge types and their knowledge-rich semantic annotations, can be encoded in a numeric format that is usable by GNNs. Given this, the architecture of two generic graph embedding models from the literature is adapted to enable their usage as a similarity measure for similarity-based retrieval. Thereby, one of the two models is more optimized towards fast similarity prediction, while the other model is optimized towards knowledge-intensive, more expressive predictions. The evaluation examines the quality and performance of these models in preselecting retrieval candidates and in approximating the ground-truth similarities of a graph-matching-based similarity measure for two semantic graph domains. The results show the great potential of the approach for use in a retrieval scenario, either as a preselection model or as an approximation of a graph similarity measure.
Considering Inter-Case Dependencies During Similarity-Based Retrieval in Process-Oriented Case-Based Reasoning.
Kumar, R.; Schultheis, A.; Malburg, L.; Hoffmann, M.; and Bergmann, R.
In
Proceedings of the 35th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2022, Hutchinson Island, Jensen Beach, Florida, USA, 2022.
Paper
doi
link
bibtex
abstract
9 downloads
@inproceedings{kumar_dependencyretrieval_2022,
author = {Kumar, Rahol and Schultheis, Alexander and Malburg, Lukas and Hoffmann, Maximilian and Bergmann, Ralph},
title = {{Considering Inter-Case Dependencies During Similarity-Based Retrieval in Process-Oriented Case-Based Reasoning}},
year = {2022},
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.},
booktitle = {Proceedings of the 35th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2022, Hutchinson Island, Jensen Beach, Florida, USA},
url = {http://www.wi2.uni-trier.de/shared/publications/2022_Kumar_FLAIRS.pdf},
doi = {10.32473/flairs.v35i.130680}
}
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.
Integrating process management and event processing in smart factories: A systems architecture and use cases.
Seiger, R.; Malburg, L.; Weber, B.; and Bergmann, R.
Journal of Manufacturing Systems, 63: 575–592. 2022.
Paper
doi
link
bibtex
abstract
21 downloads
@article{seiger_process_management_smart_factories_2022,
author = {Seiger, Ronny and Malburg, Lukas and Weber, Barbara and Bergmann, Ralph},
year = {2022},
title = {{Integrating process management and event processing in smart factories: A systems architecture and use cases}},
pages = {575--592},
volume = {63},
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.},
journal = {{Journal of Manufacturing Systems}},
url = {http://www.wi2.uni-trier.de/shared/publications/2022_Seiger_JOMS.pdf},
doi = {10.1016/j.jmsy.2022.05.012}
}
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.
SensorStream: An XES Extension for Enriching Event Logs with IoT-Sensor Data.
Grüger, J.; Malburg, L.; Mangler, J.; Bertrand, Y.; Rinderle-Ma, S.; Bergmann, R.; and Serral Asensio, E.
CoRR, abs/2206.11392. 2022.
Paper
link
bibtex
abstract
9 downloads
@article{Grueger.2022_SensorStreamExtension,
author = {Joscha Grüger and Lukas Malburg and Jürgen Mangler and Yannis Bertrand and Stefanie Rinderle-Ma and Ralph Bergmann and Estefanía {Serral Asensio}},
title = {{SensorStream: An XES Extension for Enriching Event Logs with IoT-Sensor Data}},
journal = {CoRR},
volume = {abs/2206.11392},
year = {2022},
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.
An IoT-Enriched Event Log for Process Mining in Smart Factories.
Malburg, L.; Grüger, J.; and Bergmann, R.
CoRR, abs/2209.02702. 2022.
Paper
link
bibtex
abstract
5 downloads
@article{Malburg.2022_IoTEnrichedEventLog,
author = {Lukas Malburg and Joscha Grüger and Ralph Bergmann},
title = {{An IoT-Enriched Event Log for Process Mining in Smart Factories}},
journal = {CoRR},
volume = {abs/2209.02702},
year = {2022},
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.