Mining business process deviance : a quest for accuracy

Nguyen, Hoang, Dumas, Marlon, La Rosa, Marcello, Maggi, Fabrizio Maria, & Suriadi, Suriadi (2014) Mining business process deviance : a quest for accuracy. In On the Move to Meaningful Internet Systems: OTM 2014 Conferences: Confederated International Conferences: CoopIS and ODBASE 2014, Proceedings [Lecture Notes in Computer Science, Volume 8841], Springer, Amantea, Italy, pp. 436-445.

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Abstract

This paper evaluates the suitability of sequence classification techniques for analyzing deviant business process executions based on event logs. Deviant process executions are those that deviate in a negative or positive way with respect to normative or desirable outcomes, such as non-compliant executions or executions that undershoot or exceed performance targets. We evaluate a range of feature types and classification methods in terms of their ability to accurately discriminate between normal and deviant executions both when deviances are infrequent (unbalanced) and when deviances are as frequent as normal executions (balanced). We also analyze the ability of the discovered rules to explain potential causes and contributing factors of observed deviances. The evaluation results show that feature types extracted using pattern mining techniques only slightly outperform those based on individual activity frequency. The results also suggest that more complex feature types ought to be explored to achieve higher levels of accuracy.

Impact and interest:

15 citations in Scopus
8 citations in Web of Science®
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ID Code: 75279
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Deviance mining, Process mining, Discriminative pattern
DOI: 10.1007/978-3-662-45563-0_25
ISBN: 978-3-662-45562-3
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600)
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 [please consult the author]
Deposited On: 19 Aug 2014 22:27
Last Modified: 22 Jun 2017 18:31

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