Unbiased, fine-grained description of processes performance from event data

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

    13 Citations (Scopus)
    238 Downloads (Pure)


    Performance is central to processes management and event data provides the most objective source for analyzing and improving performance. Current process mining techniques give only limited insights into performance by aggregating all event data for each process step. In this paper, we investigate process performance of all process behaviors without prior aggregation. We propose the performance spectrum as a simple model that maps all observed flows between two process steps together regarding their performance over time. Visualizing the performance spectrum of event logs reveals a large variety of very distinct patterns of process performance and performance variability that have not been described before. We provide a taxonomy for these patterns and a comprehensive overview of elementary and composite performance patterns observed on several real-life event logs from business processes and logistics. We report on a case study where performance patterns were central to identify systemic, but not globally visible process problems.

    Original languageEnglish
    Title of host publicationBusiness Process Management - 16th International Conference, BPM 2018, Proceedings
    EditorsMarco Montali, Ingo Weber, Mathias Weske, Jan vom Brocke
    Number of pages19
    ISBN (Electronic)978-3-319-98648-7
    ISBN (Print)9783319986470
    Publication statusPublished - 2018
    Event16th International Conference on Business Process Management (BPM 2018) - Sydney, Australia
    Duration: 9 Sep 201814 Sep 2018
    Conference number: 16

    Publication series

    NameLecture Notes in Computer Science


    Conference16th International Conference on Business Process Management (BPM 2018)
    Abbreviated titleBPM 2018
    OtherDissertation Award, Demonstration, and Industrial Track at BPM
    Internet address


    • Performance analysis
    • Process mining
    • Visual analytics


    Dive into the research topics of 'Unbiased, fine-grained description of processes performance from event data'. Together they form a unique fingerprint.

    Cite this