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

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Abstract

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
PublisherSpringer
Pages139-157
Number of pages19
ISBN (Electronic)978-3-319-98648-7
ISBN (Print)9783319986470
DOIs
Publication statusPublished - 2018
Event16th International Conference on Business Process Management, BPM 2018 - Sydney, Australia
Duration: 9 Sep 201814 Sep 2018

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume11080

Conference

Conference16th International Conference on Business Process Management, BPM 2018
CountryAustralia
CitySydney
Period9/09/1814/09/18

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Keywords

  • Performance analysis
  • Process mining
  • Visual analytics

Cite this

Denisov, V., Fahland, D., & van der Aalst, W. M. P. (2018). Unbiased, fine-grained description of processes performance from event data. In M. Montali, I. Weber, M. Weske, & J. vom Brocke (Eds.), Business Process Management - 16th International Conference, BPM 2018, Proceedings (pp. 139-157). (Lecture Notes in Computer Science; Vol. 11080). Springer. https://doi.org/10.1007/978-3-319-98648-7_9