Unsupervised event abstraction using pattern abstraction and local process models

F. Mannhardt, N. Tax

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

    4 Citations (Scopus)
    99 Downloads (Pure)

    Abstract

    Process mining analyzes business processes based on events stored in event logs. However, some recorded events may correspond to activities on a very low level of abstraction. When events are recorded on a too low level of abstraction, process discovery methods tend to generate overgeneralizing process models. Grouping low-level events to higher level activities, i.e., event abstraction, can be used to discover better process models. Existing event abstraction methods are mainly based on common sub-sequences and clustering techniques. In this paper, we propose to first discover local process models and, then, use those models to lift the event log to a higher level of abstraction. Our conjecture is that process models discovered on the obtained high-level event log return process models of higher quality: their fitness and precision scores are more balanced. We show this with preliminary results on several real-life event logs.
    Original languageEnglish
    Title of host publicationRADAR+EMISA 2017, June 12-13, 2017, Essen, Germany
    Place of Publications.l.
    PublisherCEUR-WS.org
    Pages55-63
    Number of pages9
    Publication statusPublished - 2017
    EventRADAR + EMISA 2017 - Essen, Germany
    Duration: 12 Jun 201713 Jun 2017

    Publication series

    NameCEUR Workshop Proceedings
    Volume1859
    ISSN (Print)1613-0073

    Conference

    ConferenceRADAR + EMISA 2017
    CountryGermany
    CityEssen
    Period12/06/1713/06/17

    Keywords

    • Event abstraction
    • Process discovery
    • Unsupervised learning

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