Data-driven process discovery: revealing conditional infrequent behavior from event logs

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    Abstract

    Process discovery methods automatically infer process models from event logs. Often, event logs contain so-called noise, e.g., infrequent outliers or recording errors, which obscure the main behavior of the process. Existing methods filter this noise based on the frequency of event labels: infrequent paths and activities are excluded. However, infrequent behavior may reveal important insights into the process. Thus, not all infrequent behavior should be considered as noise. This paper proposes the Data-aware Heuristic Miner (DHM), a process discovery method that uses the data attributes to distinguish infrequent paths from random noise by using classification techniques. Data- and control-flow of the process are discovered together. We show that the DHM is, to some degree, robust against random noise and reveals data-driven decisions, which are filtered by other discovery methods. The DHM has been successfully tested on several real-life event logs, two of which we present in this paper.
    Original languageEnglish
    Title of host publicationAdvanced Information Systems Engineering: 29th International Conference, CAiSE 2017, Essen, Germany, June 12-16, 2017, Proceedings
    EditorsEric Dubois, Klaus Pohl
    Place of PublicationCham
    PublisherSpringer
    Pages545-560
    Number of pages16
    ISBN (Electronic)978-3-319-59536-8
    ISBN (Print)978-3-319-59535-1
    DOIs
    Publication statusPublished - 2017

    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

    Keywords

    • Event logs
    • Noise
    • Process discovery
    • Process mining
    • Rules

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