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Unbiased, Fine-Grained Description of Processes Performance from Event Data

Part of the Lecture Notes in Computer Science book series (LNISA,volume 11080)


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.


  • Process mining
  • Performance analysis
  • Visual analytics

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  • DOI: 10.1007/978-3-319-98648-7_9
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The research leading to these results has received funding from Vanderlande Industries in the project “Process Mining in Logistics”. We thank Elena Belkina for support in the tool development.

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Correspondence to Vadim Denisov .

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Denisov, V., Fahland, D., van der Aalst, W.M.P. (2018). Unbiased, Fine-Grained Description of Processes Performance from Event Data. In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds) Business Process Management. BPM 2018. Lecture Notes in Computer Science(), vol 11080. Springer, Cham.

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