Discovering hierarchical consolidated models from process families

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    3 Downloads (Pure)

    Abstract

    Process families consist of different related variants that represent the same process. This might include, for example, processes executed similarly by different organizations or different versions of a same process with varying features. Motivated by the need to manage variability in process families, recent advances in process mining make it possible to discover, from a collection of event logs, a generic process model that explicitly describes the commonalities and differences across variants. However, existing approaches often result in flat complex models where it is hard to obtain a comparative insight into the common and different parts, especially when the family consists of a large number of process variants. This paper presents a decomposition-driven approach to discover hierarchical consolidated process models from collections of event logs. The discovered hierarchy consists of nested process fragments and allows to browse the variability at different levels of abstraction. The approach has been implemented as a plugin in ProM and was evaluated using synthetic and real-life event logs.

    Original languageEnglish
    Title of host publicationAdvanced Information Systems Engineering
    Subtitle of host publication29th International Conference, CAiSE 2017, Essen, Germany, June 12-16, 2017, Proceedings
    Place of PublicationDordrecht
    PublisherSpringer
    Pages314-329
    Number of pages16
    ISBN (Electronic)978-3-319-59536-8
    ISBN (Print)978-3-319-59535-1
    DOIs
    Publication statusPublished - 2017
    Event29th International Conference on Advanced Information Systems Engineering (CAiSE 2017) - Essen, Germany
    Duration: 12 Jun 201716 Jun 2017
    Conference number: 29
    http://caise2017.paluno.de/welcome/

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume10253 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference29th International Conference on Advanced Information Systems Engineering (CAiSE 2017)
    Abbreviated titleCAiSE'17
    CountryGermany
    CityEssen
    Period12/06/1716/06/17
    Internet address

    Keywords

    • Configurable fragments
    • Consolidated process families
    • Decomposed discovery
    • Hierarchical configurable models
    • Process mining

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