Performance mining for batch processing using the performance spectrum

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

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237 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
Country/TerritoryAustria
CityVienna
Period1/09/196/09/19

Keywords

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

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