Log delta analysis: Interpretable differencing of Business process event logs

van Beest, Nick R., Dumas, Marlon, García-Bañuelos, Luciano, & La Rosa, Marcello (2015) Log delta analysis: Interpretable differencing of Business process event logs. In 13th International Conference on Business Process Management (BPM 2015), 31 August - 3 September 2015, Innsbruck, Austria.

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Abstract

This paper addresses the problem of identifying and explaining behavioral differences between two business process event logs. The paper presents a method that, given two event logs, returns a set of statements in natural language capturing behavior that is present or frequent in one log, while absent or infrequent in the other. This log delta analysis method allows users to diagnose differences between normal and deviant executions of a process or between two versions or variants of a process. The method relies on a novel approach to losslessly encode an event log as an event structure, combined with a frequency-enhanced technique for differencing pairs of event structures. A validation of the proposed method shows that it accurately diagnoses typical change patterns and can explain differences between normal and deviant cases in a real-life log, more compactly and precisely than previously proposed methods.

Impact and interest:

6 citations in Scopus
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4 citations in Web of Science®

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164 since deposited on 01 Apr 2015
61 in the past twelve months

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ID Code: 83018
Item Type: Conference Paper
Refereed: Yes
Additional Information: Proceedings of the 13th International Conference on Business Process Management - Volume 9253
Pages 386-405
Additional URLs:
Keywords: deviance mining, log delta analysis, process mining, positive deviance, business process management
DOI: 10.1007/978-3-319-23063-4_26
ISBN: 9783319230627
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
Copyright Owner: Copyright 2015 Springer-Verlag New York, Inc
Deposited On: 01 Apr 2015 01:40
Last Modified: 29 Oct 2016 17:43

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