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https://hdl.handle.net/2440/137560
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Type: | Conference paper |
Title: | Automated Repair of Process Models Using Non-local Constraints |
Author: | Kalenkova, A. Carmona, J. Polyvyanyy, A. La Rosa, M. |
Citation: | Lecture Notes in Artificial Intelligence, 2020 / Janicki, R., Sidorova, N., Chatain, T. (ed./s), vol.12152 LNCS, pp.280-300 |
Publisher: | Springer International Publishing |
Publisher Place: | Manhattan, New York City, USA |
Issue Date: | 2020 |
Series/Report no.: | Lecture Notes in Computer Science (LNTCS) ; 12152 |
ISBN: | 9783030518301 |
ISSN: | 0302-9743 1611-3349 |
Conference Name: | 41st International Conference on Applications and Theory of Petri Nets and Concurrency (PETRI NETS) (24 Jun 2020 - 25 Jun 2020 : Paris, France (Virtual)) |
Editor: | Janicki, R. Sidorova, N. Chatain, T. |
Statement of Responsibility: | Anna Kalenkova, Josep Carmona, Artem Polyvyanyy, and Marcello La Rosa |
Abstract: | State-of-the-art process discovery methods construct free-choice process models from event logs. Hence, the constructed models do not take into account indirect dependencies between events. Whenever the input behavior is not free-choice, these methods fail to provide a precise model. In this paper, we propose a novel approach for the enhancement of free-choice process models, by adding non-free-choice constructs discovered a-posteriori via region-based techniques. This allows us to benefit from both the performance of existing process discovery methods, and the accuracy of the employed fundamental synthesis techniques. We prove that the proposed approach preserves fitness with respect to the event log, while improving the precision when indirect dependencies exist. The approach has been implemented and tested on both synthetic and real-life datasets. The results show its effectiveness in repairing process models discovered from event logs. |
Rights: | © Springer Nature Switzerland AG 2020 |
DOI: | 10.1007/978-3-030-51831-8_14 |
Grant ID: | http://purl.org/au-research/grants/arc/DP180102839 |
Published version: | https://link.springer.com/book/10.1007/978-3-030-51831-8 |
Appears in Collections: | Computer Science publications |
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