Discovery of Workflow Patterns - A Comparison of Process Discovery Algorithms

Kerstin Andree, Mai Hoang, Felix Dannenberg, Ingo Weber, Luise Pufahl

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


Process mining provides a set of techniques and algorithms to analyze, support, and improve business processes based on process execution data. Process discovery aims at deducing a representative process model of real-world execution. So far, process discovery algorithms have been mainly compared regarding their output quality but not yet with regard to their functional capabilities. The well-established workflow control flow patterns imperatively describe process behavior, originally used to compare modeling languages, but to date, not to compare discovery algorithms. In this work, we analyze a representative set of process discovery algorithms with regard to their coverage of 23 control flow patterns. For this purpose, we implemented each workflow pattern as an executable colored Petri net, simulated it, and ran various discovery algorithms on the obtained event log. A comparison of the results shows that the discovery algorithms mainly cover basic control flow patterns and iterative structures, while multi-instance, state-base, and cancellation patterns are only partially covered.

Original languageEnglish
Title of host publicationCooperative Information Systems - 29th International Conference, CoopIS 2023, Proceedings
EditorsMohamed Sellami, Walid Gaaloul, Maria-Esther Vidal, Boudewijn van Dongen, Hervé Panetto
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages18
ISBN (Print)9783031468452
StatePublished - 2024
Event29th International Conference on Cooperative Information Systems, CoopIS 2023 - Groningen, Netherlands
Duration: 30 Oct 20233 Nov 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14353 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference29th International Conference on Cooperative Information Systems, CoopIS 2023


  • Discovery Algorithms
  • Process Mining
  • Workflow Patterns


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