Weighted Violations in Alignment-Based Conformance Checking

Weighted Violations in Alignment-Based Conformance Checking. Grüger, J., Geyer, T., Kuhn, M., Braun, S. A., & Bergmann, R. In Montali, M., Senderovich, A., & Weidlich, M., editors, Process Mining Workshops, pages 289–301, Cham, 2023. Springer Nature Switzerland.
abstract bibtex

Conformance checking is a process mining technique that allows verifying the conformance of process instances to a given model. Many conformance checking algorithms provide quantitative information about the conformance of a process instance through metrics such as fitness. Fitness measures to what degree the model allows the behavior observed in the event log. Conventional fitness does not consider the individual severity of deviations. In cases where there are rules that are more important to comply with than others, fitness consequently does not take all factors into account. In the field of medicine, for example, there are guideline recommendations for clinical treatment that have information about their importance and soundness, making it essential to distinguish between them. Therefore, we introduce an alignment-based conformance checking approach that considers the importance of individual specifications and weights violations. The approach is evaluated with real patient data and evidence-based guideline recommendations. Using this approach, it was possible to integrate guideline recommendation metadata into the conformance checking process and to weight violations individually.

@InProceedings{weighted_violations_cc_2023,
author="Gr{\"u}ger, Joscha
and Geyer, Tobias
and Kuhn, Martin
and Braun, Stephan A.
and Bergmann, Ralph",
editor="Montali, Marco
and Senderovich, Arik
and Weidlich, Matthias",
title="Weighted Violations in Alignment-Based Conformance Checking",
booktitle="Process Mining Workshops",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="289--301",
abstract="Conformance checking is a process mining technique that allows verifying the conformance of process instances to a given model. Many conformance checking algorithms provide quantitative information about the conformance of a process instance through metrics such as fitness. Fitness measures to what degree the model allows the behavior observed in the event log. Conventional fitness does not consider the individual severity of deviations. In cases where there are rules that are more important to comply with than others, fitness consequently does not take all factors into account. In the field of medicine, for example, there are guideline recommendations for clinical treatment that have information about their importance and soundness, making it essential to distinguish between them. Therefore, we introduce an alignment-based conformance checking approach that considers the importance of individual specifications and weights violations. The approach is evaluated with real patient data and evidence-based guideline recommendations. Using this approach, it was possible to integrate guideline recommendation metadata into the conformance checking process and to weight violations individually.",
isbn="978-3-031-27815-0"
}

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