Performance mining for batch processing using the performance spectrum

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    3 Citations (Scopus)
    110 Downloads (Pure)

    Abstract

    Performance analysis from process event logs is a central element of business process management and improvement. Established performance analysis techniques aggregate time-stamped event data to identify bottlenecks or to visualize process performance indicators over time. These aggregation-based techniques are not able to detect and quantify the performance of time-dependent performance patterns such as batches. In this paper, we propose a first technique for mining performance features from the recently introduced performance spectrum. We present an algorithm to detect batches from event logs even in case of batches overlapping with non-batched cases, and we propose several measures to quantify batching performance. Our analysis of public real-life event logs shows that we can detect batches reliably, batching performance differs significantly across processes, across activities within a process, and our technique even allows to detect effective changes to batching policies regarding consistency of processing.

    Original languageEnglish
    Title of host publicationBusiness Process Management Workshops - BPM 2019 International Workshops, Revised Selected Papers
    EditorsChiara Di Francescomarino, Remco Dijkman, Uwe Zdun
    Place of PublicationCham
    PublisherSpringer
    Pages172-185
    Number of pages14
    ISBN (Electronic)978-3-030-37453-2
    ISBN (Print)978-3-030-37452-5
    DOIs
    Publication statusPublished - 2019
    Event15th International Workshop on Business Process Intelligence (BPI’19) - Vienna, Austria
    Duration: 1 Sep 20196 Sep 2019

    Publication series

    NameLecture Notes in Business Information Processing
    Volume362 LNBIP
    ISSN (Print)1865-1348
    ISSN (Electronic)1865-1356

    Conference

    Conference15th International Workshop on Business Process Intelligence (BPI’19)
    Abbreviated titleBPI'19
    CountryAustria
    CityVienna
    Period1/09/196/09/19

    Keywords

    • Batch processing
    • Performance mining
    • Performance spectrum
    • Process mining

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