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Incorporating Negative Information in Process Discovery

  • Hernan Ponce-de-LeónEmail author
  • Josep Carmona
  • Seppe K. L. M. vanden Broucke
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9253)

Abstract

The discovery of a formal process model from event logs describing real process executions is a challenging problem that has been studied from several angles. Most of the contributions consider the extraction of a model as a semi-supervised problem where only positive information is available. In this paper we present a fresh look at process discovery where also negative information can be taken into account. This feature may be crucial for deriving process models which are not only simple, fitting and precise, but also good on generalizing the right behavior underlying an event log. The technique is based on numerical abstract domains and Satisfiability Modulo Theories (SMT), and can be combined with any process discovery technique. As an example, we show in detail how to supervise a recent technique that uses numerical abstract domains. Experiments performed in our prototype implementation show the effectiveness of the techniques and the ability to improve the results produced by selected discovery techniques.

Keywords

Positive Information Negative Information Convex Polyhedron Business Process Management Inductive Logic Programming 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Hernan Ponce-de-León
    • 1
    Email author
  • Josep Carmona
    • 2
  • Seppe K. L. M. vanden Broucke
    • 3
  1. 1.Department of Computer Science and Engineering, School of Science, Helsinki Institute for Information Technology HIITAalto UniversityEspooFinland
  2. 2.Universitat Politecnica de CatalunyaBarcelonaSpain
  3. 3.Department of Decision Sciences and Information Management, Faculty of Economics and BusinessKU LeuvenLeuvenBelgium

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