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@article{Malburg_MAPEK_Loops_2023, author = {Lukas Malburg and Maximilian Hoffmann and Ralph Bergmann}, title = {{Applying MAPE-K control loops for adaptive workflow management in smart factories}}, journal = {{Journal of Intelligent Information Systems}}, keywords = {{Complex event processing, Automated planning, Cyber-physical environments, Smart factories, Adaptive workflow management, Process adaptation}}, year = {2023}, doi = {10.1007/s10844-022-00766-w}, 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.}, url = {http://www.wi2.uni-trier.de/shared/publications/2023_MalburgEtAl_MAPEK_Loops.pdf} }
@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} }
@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} }
@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} }
@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} }
@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} }
@inproceedings{malburg_gpuretrieval_2021, author = {Malburg, Lukas and Hoffmann, Maximilian and Trumm, Simon and Bergmann, Ralph}, title = {{Improving Similarity-Based Retrieval Efficiency by Using Graphic Processing Units in Case-Based Reasoning}}, year = {2021}, doi={10.32473/flairs.v34i1.128345}, url = {https://doi.org/10.32473/flairs.v34i1.128345}, 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.}, booktitle = {Proceedings of the 34th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2021, North Miami Beach, Florida, USA}, url = {http://www.wi2.uni-trier.de/shared/publications/2021_MalburgEtAl_ImprovingRetrievalByGPUs.pdf}, note = {Best Student Paper.} }
@article{Hoffmann.2021_InformedMLCBR, author = {Maximilian Hoffmann and Ralph Bergmann}, title = {Informed Machine Learning for Improved Similarity Assessment in Process-Oriented Case-Based Reasoning}, journal = {CoRR}, volume = {abs/2106.15931}, year = {2021}, url = {https://arxiv.org/pdf/2106.15931.pdf}, archivePrefix = {arXiv}, eprint = {2106.15931}, note={Presented at the IJCAI-21 workshop on Deep Learning, Case-Based Reasoning, and AutoML: Present and Future Synergies, Montreal, August 21-26} }
@inproceedings{hoffmann_graph_embedding_2020, author = {Maximilian Hoffmann and Lukas Malburg and Patrick Klein and Ralph Bergmann}, editor = {Ian Watson and Rosina O. Weber}, title = {{Using Siamese Graph Neural Networks for Similarity-Based Retrieval in Process-Oriented Case-Based Reasoning}}, booktitle = {Case-Based Reasoning Research and Development - 28th International Conference, {ICCBR} 2020, Salamanca, Spain, June 8-12, 2020, Proceedings}, series = {Lecture Notes in Computer Science}, volume = {12311}, pages = {229--244}, publisher = {Springer}, year = {2020}, url = {http://www.wi2.uni-trier.de/shared/publications/2020_ICCBR__Workflow_Graph_Embedding.pdf}, doi = {10.1007/978-3-030-58342-2\_15}, 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.}, note = {The original publication is available at www.springerlink.com} }
@inproceedings{zeyen_nutrients_2018, series = {Lecture {Notes} in {Artificial} {Intelligence}}, title = {{Considering Nutrients during the Generation of Recipes by Process-Oriented Case-based Reasoning}}, url = {http://www.wi2.uni-trier.de/shared/publications/2018_ZeyenHoffmannMuellerBergmann_ICCBR.pdf}, booktitle = {Case-{Based} {Reasoning} {Research} and {Development}: 26th {International} {Conference}, {ICCBR} 2018, {Stockholm}, {Sweden}, {July} 9-12, 2018, {Proceedings}}, publisher = {Springer}, author = {Zeyen, Christian and Hoffmann, Maximilian and M{\"u}ller, Gilbert and Bergmann, Ralph}, editor = {Cox, Michael T. and Funk, Peter and Begum, Shahina}, year = {2018}, volume = {11156}, pages = {464--479}, note = {The original publication is available at www.springerlink.com} }