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International Conference on Business Process Management

BPM 2009: Business Process Management pp 143–158Cite as

Discovering Process Models from Unlabelled Event Logs

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5701))

Abstract

Existing process mining techniques are able to discover process models from event logs where each event is known to have been produced by a given process instance. In this paper we remove this restriction and address the problem of discovering the process model when the event log is provided as an unlabelled stream of events. Using a probabilistic approach, it is possible to estimate the model by means of an iterative Expectaction–Maximization procedure. The same procedure can be used to find the case id in unlabelled event logs. A series of experiments show how the proposed technique performs under varying conditions and in the presence of certain workflow patterns. Results are presented for a running example based on a technical support process.

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Author information

Authors and Affiliations

  1. IST, Technical University of Lisbon, Portugal

    Diogo R. Ferreira

  2. Swedish Institute of Computer Science (SICS), Sweden

    Daniel Gillblad

Authors
  1. Diogo R. Ferreira
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  2. Daniel Gillblad
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Editor information

Editors and Affiliations

  1. Hewlett-Packard Laboratories, 1501 Page Mill Rd., 94304, Palo Alto, CA, USA

    Umeshwar Dayal

  2. Alps Adria University Klagenfurt, Universitätsstr. 65, 9020, Klagenfurt, Austria

    Johann Eder

  3. IBM Zurich Research Laboratory, Saeumerstr. 4, 8803, Rüschlikon, Switzerland

    Jana Koehler

  4. Eindhoven University of Technology, PO Box 513, 5600, Eindhoven, MB, The Netherlands

    Hajo A. Reijers

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© 2009 Springer-Verlag Berlin Heidelberg

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Ferreira, D.R., Gillblad, D. (2009). Discovering Process Models from Unlabelled Event Logs. In: Dayal, U., Eder, J., Koehler, J., Reijers, H.A. (eds) Business Process Management. BPM 2009. Lecture Notes in Computer Science, vol 5701. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03848-8_11

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  • DOI: https://doi.org/10.1007/978-3-642-03848-8_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03847-1

  • Online ISBN: 978-3-642-03848-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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