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)


    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
    Number of pages15
    ISBN (Electronic)978-3-030-00847-5
    ISBN (Print)978-3-030-00846-8
    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


    Conference37th International Conference on Conceptual Modeling, ER 2018


    • HCI
    • Process discovery
    • Process mining


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