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Repairing Event Logs Using Timed Process Models
Andreas Rogge-Solti1, Ronny S. Mans2, Wil M.P. van der Aalst2, and Mathias Weske1
1Hasso Plattner Institute at the University of Potsdam, Prof.-Dr.-Helmert-Strasse 2-3 14482, Potsdam, Germany
andreas.rogge-solti@hpi.uni-potsdam.de
mathias.weske@hpi.uni-potsdam.de
2Department of Information Systems, Eindhoven University of Technology, P.O. Box 513, NL-5600 MB, Eindhoven, The Netherlands
r.s.mans@tue.nl
w.m.p.v.d.aalst@tue.nl
Abstract. Process mining aims to infer meaningful insights from process-related data and attracted the attention of practitioners, tool-vendors, and researchers in recent years. Traditionally, event logs are assumed to describe the as-is situation. But this is not necessarily the case in environments where logging may be compromised due to manual logging. For example, hospital staff may need to manually enter information regarding the patient’s treatment. As a result, events or timestamps may be missing or incorrect.
In this work, we make use of process knowledge captured in process models, and provide a method to repair missing events in the logs. This way, we facilitate analysis of incomplete logs. We realize the repair by combining stochastic Petri nets, alignments, and Bayesian networks.
Keywords: process mining, missing data, stochastic Petri nets, Bayesian networks
LNCS 8186, p. 705 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2013