Improving alignment computation using model-based preprocessing

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Abstract

Alignments are a fundamental approach in conformance checking to provide an explicit relation between traces of events observed in an event log and execution sequences of process models. They are robust against intricacies in process models such as duplicate labels and invisible transitions, but at the same time computing them is a time consuming task. In this paper, we argue that precomputed rules may be leveraged to improve on the time needed to compute alignments. To this end, we utilize both structural and behavioral properties of process models to derive rules and we compare events against these rules. A violation in one of the rules indicates a problem in the event. Before alignments are computed, we mark the problematic events as so-called splitpoints. We evaluated this approach on real-life logs as well as benchmarking logs, and the results show that the proposed approach is faster than existing alignment approaches.

Original languageEnglish
Title of host publicationProceedings - 2019 International Conference on Process Mining, ICPM 2019
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages73-80
Number of pages8
ISBN (Electronic)9781728109190
DOIs
Publication statusPublished - 1 Jun 2019
Event1st International Conference on Process Mining, ICPM 2019 - Aachen, Germany
Duration: 24 Jun 201926 Jun 2019

Conference

Conference1st International Conference on Process Mining, ICPM 2019
CountryGermany
CityAachen
Period24/06/1926/06/19

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Keywords

  • Alignments
  • Conformance checking
  • Process mining

Cite this

Syamsiyah, A., & van Dongen, B. F. (2019). Improving alignment computation using model-based preprocessing. In Proceedings - 2019 International Conference on Process Mining, ICPM 2019 (pp. 73-80). [8786043] Piscataway: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICPM.2019.00021