Entropia: A family of entropy-based conformance checking measures for process mining
AuthorPolyvyanyy, A; Alkhammash, H; Di Ciccio, C; García-Bañuelos, L; Kalenkova, A; Leemans, SJJ; Mendling, J; Moffat, A; Weidlich, M
Source TitleCEUR Workshop Proceedings
PublisherCEUR Workshop Proceedings
University of Melbourne Author/sKalenkova, Anna; Moffat, Alistair; Polyvyanyy, Artem; Alkhammash, Hanan
AffiliationComputing and Information Systems
Document TypeConference Paper
CitationsPolyvyanyy, A., Alkhammash, H., Di Ciccio, C., García-Bañuelos, L., Kalenkova, A., Leemans, S. J. J., Mendling, J., Moffat, A. & Weidlich, M. (2020). Entropia: A family of entropy-based conformance checking measures for process mining. Proceedings of the ICPM Doctoral Consortium and Tool Demonstration Track 2020 co-located with the 2nd International Conference on Process Mining (ICPM 2020), 2703, pp.39-42. CEUR Workshop Proceedings.
Access StatusOpen Access
Open Access URLhttp://ceur-ws.org/Vol-2703/paperTD6.pdf
ARC Grant codeARC/DP180102839
This paper presents a command-line tool, called Entropia, that implements a family of conformance checking measures for process mining founded on the notion of entropy from information theory. The measures allow quantifying classical non-deterministic and stochastic precision and recall quality criteria for process models automatically discovered from traces executed by IT-systems and recorded in their event logs. A process model has "good" precision with respect to the log it was discovered from if it does not encode many traces that are not part of the log, and has "good" recall if it encodes most of the traces from the log. By definition, the measures possess useful properties and can often be computed quickly.
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