Analyzing event logs generated during the execution of digital processes, organizations can monitor the behavior of dysfunctional or unspecified processes. For achieving the most refined results, high-quality and up-to-date process models are required. However, the selection of the proper process discovery algorithm is often addressed by human experts that can relate quality criteria, event logs behavior, and discovery techniques. Exploiting a meta-learning approach, we created a procedure that identifies the optimal discovery technique based on a user-defined balance of quality metrics. Our experiments exploited 1091 event logs representing extensive possible business process behaviors. Given a set of available algorithms, we obtained an F-score of 0.76 for recommending the discovery algorithm that maximizes quality criteria. Moreover, our method supports a more in-depth investigation of the process discovery problem by mapping log behavior and discovery techniques.

Automating Process Discovery Through Meta-learning / G. Marques Tavares, S. Barbon Junior, E. Damiani (LECTURE NOTES IN COMPUTER SCIENCE). - In: Cooperative Information Systems / [a cura di] M. Sellami, P. Ceravolo, H.A. Reijers, W. Gaaloul, H. Panetto. - [s.l] : Springer, 2022. - ISBN 9783031178337. - pp. 205-222 (( Intervento presentato al 28. convegno International Conference on Cooperative Information Systems tenutosi a Bolzano nel 2022 [10.1007/978-3-031-17834-4_12].

Automating Process Discovery Through Meta-learning

G. Marques Tavares
Primo
;
E. Damiani
Ultimo
2022

Abstract

Analyzing event logs generated during the execution of digital processes, organizations can monitor the behavior of dysfunctional or unspecified processes. For achieving the most refined results, high-quality and up-to-date process models are required. However, the selection of the proper process discovery algorithm is often addressed by human experts that can relate quality criteria, event logs behavior, and discovery techniques. Exploiting a meta-learning approach, we created a procedure that identifies the optimal discovery technique based on a user-defined balance of quality metrics. Our experiments exploited 1091 event logs representing extensive possible business process behaviors. Given a set of available algorithms, we obtained an F-score of 0.76 for recommending the discovery algorithm that maximizes quality criteria. Moreover, our method supports a more in-depth investigation of the process discovery problem by mapping log behavior and discovery techniques.
Process discovery; Meta-learning; Model quality; Recommendation; Process mining
Settore INF/01 - Informatica
2022
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/954751
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