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Discovering Process Models from Unlabelled Event Logs

  • Diogo R. Ferreira
  • Daniel Gillblad
Part of the Lecture Notes in Computer Science book series (LNCS, 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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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Diogo R. Ferreira
    • 1
  • Daniel Gillblad
    • 2
  1. 1.ISTTechnical University of LisbonPortugal
  2. 2.Swedish Institute of Computer Science (SICS)Sweden

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