Incorporating Negative Information in Process Discovery

19 Pages Posted: 12 Apr 2015

See all articles by Hernan Ponce de Leon

Hernan Ponce de Leon

Helsinki Institute for Information Technology

Josep Carmona

Polytechnic University of Catalonia (UPC)

Seppe Vanden Broucke

KU Leuven. Faculty of Business and Economics (FBE)

Date Written: March 2015

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.

Suggested Citation

Ponce de Leon, Hernan and Carmona, Josep and Vanden Broucke, Seppe, Incorporating Negative Information in Process Discovery (March 2015). Available at SSRN: https://ssrn.com/abstract=2592996 or http://dx.doi.org/10.2139/ssrn.2592996

Hernan Ponce de Leon (Contact Author)

Helsinki Institute for Information Technology ( email )

Helsinki 00180
Finland

Josep Carmona

Polytechnic University of Catalonia (UPC) ( email )

C. Jordi Girona, 31
Barcelona, 08034
Spain

Seppe Vanden Broucke

KU Leuven. Faculty of Business and Economics (FBE) ( email )

Naamsestraat 69
Leuven, B-3000
Belgium

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