Publications
2026
Hotz, Maxim; Malburg, Lukas; Thanabalan, Kokulan; Bergmann, Ralph
Vision-Based Retrieval of Semantic Workflows in Process-Oriented Case-Based Reasoning Proceedings Article
In: Malburg, Lukas; Bach, Kerstin (Ed.): Case-Based Reasoning Research and Development, Springer., 2026, (Accepted for publication.).
Abstract | BibTeX | Tags: Process-Oriented Case-Based Reasoning, Semantic Workflows, Similarity Assessment, Vision Transformer, Vision-Based Retrieval
@inproceedings{HotzEtAl2026VisionBasedRetrieval,
title = {Vision-Based Retrieval of Semantic Workflows in Process-Oriented Case-Based Reasoning},
author = {Maxim Hotz and Lukas Malburg and Kokulan Thanabalan and Ralph Bergmann},
editor = {Lukas Malburg and Kerstin Bach},
year = {2026},
date = {2026-01-01},
booktitle = {Case-Based Reasoning Research and Development},
publisher = {Springer.},
series = {Lecture Notes in Computer Science},
abstract = {Process-Oriented Case-Based Reasoning (POCBR) relies on retrieving similar semantic workflow graphs, but accurate retrieval typically requires computationally expensive graph matching. To improve scalability, existing systems often employ two-stage retrieval strategies such as MAC/FAC, where a fast but less accurate prefilter reduces the search space before detailed matching. Recent approaches use Graph Neural Networks (GNNs) for this prefiltering step, but they still involve trade-offs between approximation quality, efficiency, and interpretability. This paper investigates whether semantic workflows can instead be retrieved by visual comparison. To this end, we analyze existing graph visualization strategies regarding their suitability for vision-based retrieval and propose a multi-step pipeline that transforms semantic workflows into deterministic visual representations. These representations are then used in combination with modern vision models for similarity-based retrieval. Experiments in two POCBR domains indicate that the proposed approach yields substantial speedups while producing retrieval quality that is generally below but still competitive with established methods. The results demonstrate that vision-based retrieval is a promising alternative for use as a MAC-stage filter in POCBR.},
note = {Accepted for publication.},
keywords = {Process-Oriented Case-Based Reasoning, Semantic Workflows, Similarity Assessment, Vision Transformer, Vision-Based Retrieval},
pubstate = {published},
tppubtype = {inproceedings}
}
Process-Oriented Case-Based Reasoning (POCBR) relies on retrieving similar semantic workflow graphs, but accurate retrieval typically requires computationally expensive graph matching. To improve scalability, existing systems often employ two-stage retrieval strategies such as MAC/FAC, where a fast but less accurate prefilter reduces the search space before detailed matching. Recent approaches use Graph Neural Networks (GNNs) for this prefiltering step, but they still involve trade-offs between approximation quality, efficiency, and interpretability. This paper investigates whether semantic workflows can instead be retrieved by visual comparison. To this end, we analyze existing graph visualization strategies regarding their suitability for vision-based retrieval and propose a multi-step pipeline that transforms semantic workflows into deterministic visual representations. These representations are then used in combination with modern vision models for similarity-based retrieval. Experiments in two POCBR domains indicate that the proposed approach yields substantial speedups while producing retrieval quality that is generally below but still competitive with established methods. The results demonstrate that vision-based retrieval is a promising alternative for use as a MAC-stage filter in POCBR.
2025
Hotz, Maxim; Malburg, Lukas; Bergmann, Ralph
Advanced Search Techniques for Determining Optimal Sequences of Adaptation Rules in Process-Oriented Case-Based Reasoning Proceedings Article
In: Bichindaritz, Isabelle; Lopez, Beatriz (Ed.): Case-Based Reasoning Research and Development - 33rd International Conference, ICCBR 2025, Biarritz, France, June 30 - July 3, 2025, Proceedings, pp. 236–251, Springer., 2025.
Abstract | Links | BibTeX | Tags: Adaptive Workflow Management, Constrained Optimization Planning, Genetic Algorithms, Process-Oriented Case-Based Reasoning, Rule-Based Adaptation
@inproceedings{Hotz_AdvancedSearchForRules_2025,
title = {Advanced Search Techniques for Determining Optimal Sequences of Adaptation Rules in Process-Oriented Case-Based Reasoning},
author = {Maxim Hotz and Lukas Malburg and Ralph Bergmann},
editor = {Isabelle Bichindaritz and Beatriz Lopez},
url = {https://www.wi2.uni-trier.de/shared/publications/2025_ICCBR_AdvancedSearchForRules_HotzEtAl.pdf},
doi = {10.1007/978-3-031-96559-3_16},
year = {2025},
date = {2025-01-01},
booktitle = {Case-Based Reasoning Research and Development - 33rd International Conference, ICCBR 2025, Biarritz, France, June 30 - July 3, 2025, Proceedings},
volume = {15662},
pages = {236–251},
publisher = {Springer.},
series = {Lecture Notes in Computer Science},
abstract = {The application of adaptation knowledge still poses a major challenge in modern Case-Based Reasoning (CBR) systems. This is especially the case in the subdomain of Process-Oriented Case-Based Reasoning (POCBR), where cases represent procedural experimental knowledge. Current adaptation methods in this field make use of proprietary local search techniques to apply adaptation knowledge, which is inefficient and does not allow exploitation of advanced search and optimization techniques. Therefore, this work presents an approach to transform rule-based adaptation into a planning problem that is solvable with both Constrained Optimization Planning (COP) and Genetic Algorithms (GA). The results of an experimental evaluation indicate that this transformation is feasible, although it does not achieve significant improvements in terms of adaptation quality without fine-tuning the default search parameters. However, integration into state-of-the-art planning frameworks builds the basis for using a variety of additional features and updates, enhancing the modeling and configuration process.},
keywords = {Adaptive Workflow Management, Constrained Optimization Planning, Genetic Algorithms, Process-Oriented Case-Based Reasoning, Rule-Based Adaptation},
pubstate = {published},
tppubtype = {inproceedings}
}
The application of adaptation knowledge still poses a major challenge in modern Case-Based Reasoning (CBR) systems. This is especially the case in the subdomain of Process-Oriented Case-Based Reasoning (POCBR), where cases represent procedural experimental knowledge. Current adaptation methods in this field make use of proprietary local search techniques to apply adaptation knowledge, which is inefficient and does not allow exploitation of advanced search and optimization techniques. Therefore, this work presents an approach to transform rule-based adaptation into a planning problem that is solvable with both Constrained Optimization Planning (COP) and Genetic Algorithms (GA). The results of an experimental evaluation indicate that this transformation is feasible, although it does not achieve significant improvements in terms of adaptation quality without fine-tuning the default search parameters. However, integration into state-of-the-art planning frameworks builds the basis for using a variety of additional features and updates, enhancing the modeling and configuration process.
2023
Schultheis, Alexander; Hoffmann, Maximilian; Malburg, Lukas; Bergmann, Ralph
Explanation of Similarities in Process-Oriented Case-Based Reasoning by Visualization Proceedings Article
In: Case-Based Reasoning Research and Development - 31st International Conference, ICCBR 2023, Aberdeen, Scotland, July 17-20, 2023, Proceedings, pp. 53–68, Springer, 2023.
Abstract | Links | BibTeX | Tags: Explainable Case-Based Reasoning, Explanation, Process-Oriented Case-Based Reasoning, Similarity, Visualization
@inproceedings{SchultheisHMB2023,
title = {Explanation of Similarities in Process-Oriented Case-Based Reasoning by Visualization},
author = {Alexander Schultheis and Maximilian Hoffmann and Lukas Malburg and Ralph Bergmann},
url = {https://www.wi2.uni-trier.de/shared/publications/2023_ICCBR_SchultheisHMB.pdf},
doi = {10.1007/978-3-031-40177-0_4},
year = {2023},
date = {2023-01-01},
booktitle = {Case-Based Reasoning Research and Development - 31st International Conference, ICCBR 2023, Aberdeen, Scotland, July 17-20, 2023, Proceedings},
volume = {14141},
pages = {53–68},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
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 = {Explainable Case-Based Reasoning, Explanation, Process-Oriented Case-Based Reasoning, Similarity, Visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
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.