A visual approach to spot statistically-significant differences in event logs based on process metrics

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    Abstract

    This paper addresses the problem of comparing different variants of the same process. We aim to detect relevant differences between processes based on what was recorded in event logs. We use transition systems to model behavior and to highlight differences. Transition systems are annotated with measurements, used to compare the behavior in the variants. The results are visualized as transitions systems, which are colored to pinpoint the significant differences. The approach has been implemented in ProM, and the implementation is publicly available. We validated our approach by performing experiments using real-life event data. The results show how our technique is able to detect relevant differences undetected by previous approaches while it avoids detecting insignificant differences.

    Original languageEnglish
    Title of host publicationAdvanced Information Systems Engineering
    Subtitle of host publication28th International Conference, CAiSE 2016, Ljubljana, Slovenia, June 13-17, 2016. Proceedings
    EditorsS. Nurcan, P. Soffer, M. Bajec, J. Eder
    Place of PublicationDordrecht
    PublisherSpringer
    Pages151-166
    Number of pages16
    ISBN (Electronic)978-3-319-39696-5
    ISBN (Print)978-3-319-39695-8
    DOIs
    Publication statusPublished - 2016
    Event28th International Conference on Advanced Information Systems Engineering (CAiSE 2016) - Ljubljana, Slovenia
    Duration: 13 Jun 201617 Jun 2016
    Conference number: 28
    http://caise2016.si/

    Publication series

    NameLecture Notes in Computer Science
    Volume9694
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Conference

    Conference28th International Conference on Advanced Information Systems Engineering (CAiSE 2016)
    Abbreviated titleCAiSE '16
    CountrySlovenia
    CityLjubljana
    Period13/06/1617/06/16
    Other"Information Systems for Connecting People"
    Internet address

    Keywords

    • Annotated transition system
    • Process mining
    • Process variants comparison
    • Statistical significance

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