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Process Discovery Using Prior Knowledge

  • Aubrey J. Rembert
  • Amos Omokpo
  • Pietro Mazzoleni
  • Richard T. Goodwin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8274)

Abstract

In this paper, we describe a process discovery algorithm that leverages prior knowledge and process execution data to learn a control-flow model. Most process discovery algorithms are not able to exploit prior knowledge supplied by a domain expert. Our algorithm incorporates prior knowledge using ideas from Bayesian statistics. We demonstrate that our algorithm is able to recover a control-flow model in the presence of noisy process execution data, and uncertain prior knowledge.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Aubrey J. Rembert
    • 1
  • Amos Omokpo
    • 1
  • Pietro Mazzoleni
    • 1
  • Richard T. Goodwin
    • 1
  1. 1.IBM T.J. Watson Research CenterUSA

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