Improving alignment computation using model-based preprocessing

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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.

Originele taal-2Engels
TitelProceedings - 2019 International Conference on Process Mining, ICPM 2019
Plaats van productiePiscataway
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's73-80
Aantal pagina's8
ISBN van elektronische versie9781728109190
DOI's
StatusGepubliceerd - 1 jun 2019
Evenement1st International Conference on Process Mining, ICPM 2019 - Aachen, Duitsland
Duur: 24 jun 201926 jun 2019

Congres

Congres1st International Conference on Process Mining, ICPM 2019
LandDuitsland
StadAachen
Periode24/06/1926/06/19

Vingerafdruk

Benchmarking
Labels
Alignment
Process model
Violations

Citeer dit

Syamsiyah, A., & van Dongen, B. F. (2019). Improving alignment computation using model-based preprocessing. In Proceedings - 2019 International Conference on Process Mining, ICPM 2019 (blz. 73-80). [8786043] Piscataway: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICPM.2019.00021
Syamsiyah, Alifah ; van Dongen, Boudewijn F. / Improving alignment computation using model-based preprocessing. Proceedings - 2019 International Conference on Process Mining, ICPM 2019. Piscataway : Institute of Electrical and Electronics Engineers, 2019. blz. 73-80
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title = "Improving alignment computation using model-based preprocessing",
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.",
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Syamsiyah, A & van Dongen, BF 2019, Improving alignment computation using model-based preprocessing. in Proceedings - 2019 International Conference on Process Mining, ICPM 2019., 8786043, Institute of Electrical and Electronics Engineers, Piscataway, blz. 73-80, Aachen, Duitsland, 24/06/19. https://doi.org/10.1109/ICPM.2019.00021

Improving alignment computation using model-based preprocessing. / Syamsiyah, Alifah; van Dongen, Boudewijn F.

Proceedings - 2019 International Conference on Process Mining, ICPM 2019. Piscataway : Institute of Electrical and Electronics Engineers, 2019. blz. 73-80 8786043.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

TY - GEN

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