Impact-driven process model repair

Polyvyanyy, Artem, van der Aalst, Wil M.P., ter Hofstede, Arthur H.M., & Wynn, Moe T. (2016) Impact-driven process model repair. ACM Transactions on Software Engineering and Methodology (TOSEM), 25(4), p. 28.

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The abundance of event data in today's information systems makes it possible to "confront" process models with the actual observed behavior. Process mining techniques use event logs to discover process models that describe the observed behavior, and to check conformance of process models by diagnosing deviations between models and reality. In many situations it is desirable to mediate between a preexisting model and observed behavior. Hence, we would like to repair the model while improving the correspondence between model and log as much as possible. The approach presented in this paper assigns predefined costs to repair actions (allowing inserting or skipping of activities). Given a maximum degree of change, we search for models that are optimal in terms of fitness, i.e., the fraction of behavior in the log not possible according to the model is minimized. To compute fitness we need to align the model and log and this can be time consuming. Hence, finding an optimal repair may be intractable. We propose different alternative approaches to speed-up repair. The number of alignment computations can be reduced dramatically while still returning near-optimal repairs. The different approaches have been implemented using the process mining framework ProM and evaluated using real-life logs.

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ID Code: 97668
Item Type: Journal Article
Refereed: Yes
Additional URLs:
Keywords: Process mining, Process model repair, Repair recommendation, Process model, Event log
DOI: 10.1145/2980764
ISSN: 1557-7392
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > NUMERICAL AND COMPUTATIONAL MATHEMATICS (010300) > Optimisation (010303)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > DISTRIBUTED COMPUTING (080500)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600)
Divisions: Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2016 ACM
Copyright Statement: This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in PUBLICATION, {VOL#, ISS#, (DATE)}
Deposited On: 26 Jul 2016 23:41
Last Modified: 19 Oct 2016 05:02

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