Modeling and discovering cancelation behavior

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    7 Citations (Scopus)
    2 Downloads (Pure)

    Abstract

    This paper presents a novel extension to the process tree model to support cancelation behavior, and proposes a novel process discovery technique to discover sound, fitting models with cancelation features. The proposed discovery technique relies on a generic error oracle function, and allows us to discover complex combinations of multiple, possibly nested cancelation regions based on observed behavior. An implementation of the proposed approach is available as a ProM plugin. Experimental results based on real-life event logs demonstrate the feasibility and usefulness of the approach.

    Original languageEnglish
    Title of host publicationOn the Move to Meaningful Internet Systems. OTM 2017 Conferences
    Subtitle of host publicationConfederated International Conferences: CoopIS, C&TC, and ODBASE 2017, Rhodes, Greece, October 23-27, 2017, Proceedings, Part I
    EditorsH. Panetto, C. Debruyne, W. Galoul, M. Papazoglou, A. Paschke, C. Agostino Ardagna
    Place of PublicationDordrecht
    PublisherSpringer
    Pages93-113
    Number of pages21
    ISBN (Electronic)978-3-319-69462-7
    ISBN (Print)978-3-319-69461-0
    DOIs
    Publication statusPublished - 2017
    EventConfederated International Conference On the Move to Meaningful Internet Systems, OTM 2017 held in conjunction with Conferences on CoopIS, CandTC and ODBASE 2017 - Rhodes, Greece
    Duration: 23 Sep 201727 Sep 2017

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume10573 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    ConferenceConfederated International Conference On the Move to Meaningful Internet Systems, OTM 2017 held in conjunction with Conferences on CoopIS, CandTC and ODBASE 2017
    CountryGreece
    CityRhodes
    Period23/09/1727/09/17

    Keywords

    • Cancelation discovery
    • Cancelation modeling
    • Event logs
    • Process discovery
    • Process mining
    • Process trees
    • Reset nets

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