Publications
2026
Schander, Roman; Schultheis, Alexander; Bergmann, Ralph
Explanation of Similarities in Temporal Case-Based Reasoning by Visualization Proceedings Article
In: Case-Based Reasoning Research and Development - 34th International Conference, ICCBR 2026, Bremen, Germany, August 13 - 16, 2026, Proceedings, Springer, 2026, (Accepted for Publication.).
Abstract | BibTeX | Tags: Explainable Case-Based Reasoning, Mapping-Based Time Series Similarity, Temporal Case-Based Reasoning, Visualization
@inproceedings{SchanderSB2026,
title = {Explanation of Similarities in Temporal Case-Based Reasoning by Visualization},
author = {Roman Schander and Alexander Schultheis and Ralph Bergmann},
year = {2026},
date = {2026-01-01},
booktitle = {Case-Based Reasoning Research and Development - 34th International Conference, ICCBR 2026, Bremen, Germany, August 13 - 16, 2026, Proceedings},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
abstract = {Temporal Case-Based Reasoning (TCBR) relies on time series similarity measures whose global similarity values emerge from multiple local contributions, making the development, validation, and maintenance of similarity models difficult for knowledge engineers. This paper investigates visualization-based explanations for mapping-based time series similarity measures that illustrate how such values are composed. To this end, a systematic literature study on visualization approaches for time series similarities is conducted, and a requirements catalog for similarity visualizations in TCBR is derived. Existing techniques are then assessed against these requirements. Based on the results, an extended point mapping visualization is presented that makes mappings between time series entries explicit and complements them with interactive tools for exploration and inspection. The concept is implemented in the ProCAKE framework and was evaluated in an exploratory user study. The results suggest that the approach is perceived as interpretable and practically useful for supporting the understanding of time series similarity values and their local composition in TCBR.},
note = {Accepted for Publication.},
keywords = {Explainable Case-Based Reasoning, Mapping-Based Time Series Similarity, Temporal Case-Based Reasoning, Visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
Temporal Case-Based Reasoning (TCBR) relies on time series similarity measures whose global similarity values emerge from multiple local contributions, making the development, validation, and maintenance of similarity models difficult for knowledge engineers. This paper investigates visualization-based explanations for mapping-based time series similarity measures that illustrate how such values are composed. To this end, a systematic literature study on visualization approaches for time series similarities is conducted, and a requirements catalog for similarity visualizations in TCBR is derived. Existing techniques are then assessed against these requirements. Based on the results, an extended point mapping visualization is presented that makes mappings between time series entries explicit and complements them with interactive tools for exploration and inspection. The concept is implemented in the ProCAKE framework and was evaluated in an exploratory user study. The results suggest that the approach is perceived as interpretable and practically useful for supporting the understanding of time series similarity values and their local composition in TCBR.
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.