Process mining as first-order classification learning on logs with negative events

Authors:
Stijn Goedertier;David Martens;Bart Baesens;Raf Haesen;Jan Vanthienen
Affiliations:
Department of Decision Sciences & Information Management, Katholieke Universiteit Leuven, Belgium;Department of Decision Sciences & Information Management, Katholieke Universiteit Leuven, Belgium;Department of Decision Sciences & Information Management, Katholieke Universiteit Leuven, Belgium and School of Management, University of Southampton, United Kingdom;Department of Decision Sciences & Information Management, Katholieke Universiteit Leuven, Belgium and Vlekho Business School, Belgium;Department of Decision Sciences & Information Management, Katholieke Universiteit Leuven, Belgium
Venue:
BPM'07 Proceedings of the 2007 international conference on Business process management
Year:
2007

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Abstract

Process mining is the automated construction of process models from information system event logs. In this paper we identify three fundamental difficulties related to process mining: the lack of negative information, the presence of history-dependent behavior and the presence of noise. These difficulties can elegantly dealt with when process mining is represented as first-order classification learning on event logs supplemented with negative events. A first set of process discovery experiments indicates the feasibility of this learning technique.