Discovering causal factors explaining business process performance variation

B.F.A. Hompes, Abderrahmane Maaradji, M. La Rosa, M. Dumas, J.C.A.M. Buijs, W.M.P. van der Aalst

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

    12 Citations (Scopus)
    133 Downloads (Pure)


    Business process performance may be affected by a range of factors, such as the volume and characteristics of ongoing cases or the performance and availability of individual resources. Event logs collected by modern information systems provide a wealth of data about the execution of business processes. However, extracting root causes for performance issues from these event logs is a major challenge. Processes may change continuously due to internal and external factors. Moreover, there may be many resources and case attributes influencing performance. This paper introduces a novel approach based on time series analysis to detect cause-effect relations between a range of business process characteristics and process performance indicators. The scalability and practical relevance of the approach has been validated by a case study involving a real-life insurance claims handling process.
    Original languageEnglish
    Title of host publicationAdvanced Information Systems Engineering - 29th International Conference, CAiSE 2017
    EditorsEric Dubois, Klaus Pohl
    Place of PublicationEssen
    Number of pages15
    ISBN (Print)9783319595351
    Publication statusPublished - 12 Jun 2017
    Event29th International Conference on Advanced Information Systems Engineering (CAiSE 2017) - Essen, Germany
    Duration: 12 Jun 201716 Jun 2017
    Conference number: 29

    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


    Conference29th International Conference on Advanced Information Systems Engineering (CAiSE 2017)
    Abbreviated titleCAiSE'17
    Internet address


    • process mining
    • causal factor
    • time series
    • business process


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