Determining process model precision and generalization with weighted artificial negative events

Vanden Broucke, S.K.L.M, De Weerdt, J., Vanthienen , J., & Baesens, B. (2014) Determining process model precision and generalization with weighted artificial negative events. IEEE Transactions on Knowledge and Data Engineering, 26(8), pp. 1877-1889.

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Abstract

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 towards 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.

Impact and interest:

10 citations in Scopus
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3 citations in Web of Science®

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ID Code: 61561
Item Type: Journal Article
Refereed: Yes
Keywords: process mining, conformance checking, artificial negative events, precision, generalization
DOI: 10.1109/TKDE.2013.130
ISSN: 1041-4347
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600)
Divisions: Past > Schools > Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Funding:
Deposited On: 29 Jul 2013 03:39
Last Modified: 01 Sep 2014 05:02

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