Discovering and navigating a collection of process models using multiple quality dimensions

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

6 Citations (Scopus)


Process discovery algorithms typically aim at discovering a process model from an event log that best describes the recorded behavior. However, multiple quality dimensions can be used to evaluate a process model. In previous work we showed that there often is not one single process model that describes the observed behavior best in all quality dimensions. Therefore, we present an extension to our flexible ETM algorithm that does not result in a single best process model but in a collection of mutually non-dominating process models. This is achieved by constructing a Pareto front of process models. We show by applying our approach on a real life event log that the resulting collection of process models indeed contains several good candidates. Furthermore, by presenting a collection of process models, we show that it allows the user to investigate the different trade-offs between different quality dimensions. Keywords: Process mining; Process model quality; Process model collection
Original languageEnglish
Title of host publicationBusiness Process Management Workshops (BPM 2013 International Workshops, Beijing, China, August 26, 2013, Revised Papers)
EditorsN. Lohmann, M. Song, P. Wohed
Place of PublicationBerlin
ISBN (Print)978-3-319-06256-3
Publication statusPublished - 2014
Event9th International Workshop on Business Process Intelligence (BPI 2013) - Beijing, China
Duration: 26 Aug 201326 Aug 2013
Conference number: 9

Publication series

NameLecture Notes in Business Information Processing
ISSN (Print)1865-1348


Workshop9th International Workshop on Business Process Intelligence (BPI 2013)
Abbreviated titleBPI 2013
OtherWorkshop held in conjunction with the 11th International Conference on Business Process Management (BPM 2013)


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