Business Process Deviance Mining: Review and Evaluation

Nguyen, Hoang, Dumas, Marlon, La Rosa, Marcello, Maggi, Fabrizio Maria, Suriadi, Suriadi, & Naiker, Ugen (2016) Business Process Deviance Mining: Review and Evaluation.

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Abstract

Business process deviance refers to the phenomenon whereby a subset of the executions of a business process deviate, in a negative or positive way, with respect to its expected or desirable outcomes. Deviant executions of a business process include those that violate compliance rules, or executions that undershoot or exceed performance targets. Deviance mining is concerned with uncovering the reasons for deviant executions by analyzing business process event logs. This article provides a systematic review and comparative evaluation of deviance mining approaches based on a family of data mining techniques known as sequence classification. Using real-life logs from multiple domains, we evaluate a range of feature types and classification methods in terms of their ability to accurately discriminate between normal and deviant executions of a process. We also analyze the interestingness of the rule sets extracted using different methods. We observe that feature sets extracted using pattern mining techniques only slightly outperform simpler feature sets based on counts of individual activity occurrences in a trace.

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19 since deposited on 17 Aug 2016
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ID Code: 98196
Item Type: Report
Refereed: No
Keywords: process mining, business process deviance, sequence classification
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600) > Decision Support and Group Support Systems (080605)
Divisions: Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Facilities: Science and Engineering Centre
Copyright Owner: Copyright 2016 The authors
Deposited On: 17 Aug 2016 23:51
Last Modified: 17 Jan 2017 12:23

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