Issue No. 08 - Aug. (2014 vol. 26)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2013.130
Seppe K. L. M. vanden Broucke , Department of Decision Sciences and Information Management, KU Leuven, Leuven, Belgium
Jochen De Weerdt , Department of Decision Sciences and Information Management, KU Leuven, Leuven, Belgium
Jan Vanthienen , Department of Decision Sciences and Information Management, KU Leuven, Leuven, Belgium
Bart Baesens , Department of Decision Sciences and Information Management, KU Leuven, Leuven, Belgium
Process mining encompasses the research area which is concerned with knowledge discovery from event logs. One common process mining task focuses on conformance checking, comparing discovered or designed process models with actual real-life behavior as captured in event logs in order to assess the “goodness” of the process model. This paper introduces a novel conformance checking method to measure how well a process model performs in terms of precision and generalization with respect to the actual executions of a process as recorded in an event log. Our approach differs from related work in the sense that we apply the concept of so-called weighted artificial negative events toward conformance checking, leading to more robust results, especially when dealing with less complete event logs that only contain a subset of all possible process execution behavior. In addition, our technique offers a novel way to estimate a process model's ability to generalize. Existing literature has focused mainly on the fitness (recall) and precision (appropriateness) of process models, whereas generalization has been much more difficult to estimate. The described algorithms are implemented in a number of ProM plugins, and a Petri net conformance checking tool was developed to inspect process model conformance in a visual manner.
Petri nets, data mining, generalisation (artificial intelligence)