Interactive data-driven process model construction

P. M. Dixit, H. M.W. Verbeek, J. C.A.M. Buijs, W. M.P. van der Aalst

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

    7 Citations (Scopus)

    Abstract

    Process discovery algorithms address the problem of learning process models from event logs. Typically, in such settings a user’s activity is limited to configuring the parameters of the discovery algorithm, and hence the user expertise/domain knowledge can not be incorporated during traditional process discovery. In a setting where the event logs are noisy, incomplete and/or contain uninteresting activities, the process models discovered by discovery algorithms are often inaccurate and/or incomprehensible. Furthermore, many of these automated techniques can produce unsound models and/or cannot discover duplicate activities, silent activities etc. To overcome such shortcomings, we introduce a new concept to interactively discover a process model, by combining a user’s domain knowledge with the information from the event log. The discovered models are always sound and can have duplicate activities, silent activities etc. An objective evaluation and a case study shows that the proposed approach can outperform traditional discovery techniques.

    Original languageEnglish
    Title of host publicationConceptual Modeling - 37th International Conference, ER 2018, Proceedings
    EditorsZhanhuai Li, Juan C. Trujillo, Xiaoyong Du, Mong Li Lee, Karen C. Davis, Tok Wang Ling, Guoliang Li
    Place of PublicationCham
    PublisherSpringer
    Pages251-265
    Number of pages15
    ISBN (Electronic)978-3-030-00847-5
    ISBN (Print)978-3-030-00846-8
    DOIs
    Publication statusPublished - 1 Jan 2018
    Event37th International Conference on Conceptual Modeling, ER 2018 - Xi'an, China
    Duration: 22 Oct 201825 Oct 2018

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume11157 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference37th International Conference on Conceptual Modeling, ER 2018
    CountryChina
    CityXi'an
    Period22/10/1825/10/18

    Keywords

    • HCI
    • Process discovery
    • Process mining

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