A unified approach for measuring precision and generalization based on anti-alignments

B.F. Van Dongen, J. Carmona, T. Chatain

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

    20 Citations (Scopus)
    2 Downloads (Pure)


    The holy grail in process mining is an algorithm that, given an event log, produces fitting, precise, properly generalizing and simple process models. While there is consensus on the existence of solid metrics for fitness and simplicity, current metrics for precision and generalization have important flaws, which hamper their applicability in a general setting. In this paper, a novel approach to measure precision and generalization is presented, which relies on the notion of antialignments. An anti-alignment describes highly deviating model traces with respect to observed behavior. We propose metrics for precision and generalization that resemble the leave-one-out cross-validation techniques, where individual traces of the log are removed and the computed anti-alignment assess the model’s capability to describe precisely or generalize the observed behavior. The metrics have been implemented in ProM and tested on several examples.

    Original languageEnglish
    Title of host publicationBusiness Process Management
    Subtitle of host publication14th International Conference, BPM 2016, Rio de Janeiro, Brazil, September 18-22, 2016. Proceedings
    EditorsB. La Rosa, P. Loos, O. Pastor
    Place of PublicationDordrecht
    Number of pages18
    ISBN (Electronic)978-3-319-45348-4
    ISBN (Print)978-3-319-45347-7
    Publication statusPublished - 2016

    Publication series

    NameLecture notes in computer science
    ISSN (Print)0302-9743

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