Fast and accurate business process drift detection

Maaradji, Abderrahmane, Dumas, Marlon, La Rosa, Marcello, & Ostovar, Alireza (2015) Fast and accurate business process drift detection. In Motahari-Nezhad, Hamid Reza, Recker, Jan, & Weidlich, Matthias (Eds.) Proceedings of 13th International Conference, BPM 2015, Innsbruck, Austria, Springer International Publishing, pp. 406-422.

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Business processes are prone to continuous and unexpected changes. Process workers may start executing a process differently in order to adjust to changes in workload, season, guidelines or regulations for example. Early detection of business process changes based on their event logs – also known as business process drift detection – enables analysts to identify and act upon changes that may otherwise affect process performance. Previous methods for business process drift detection are based on an exploration of a potentially large feature space and in some cases they require users to manually identify the specific features that characterize the drift. Depending on the explored feature set, these methods may miss certain types of changes. This paper proposes a fully automated and statistically grounded method for detecting process drift. The core idea is to perform statistical tests over the distributions of runs observed in two consecutive time windows. By adaptively sizing the window, the method strikes a trade-off between classification accuracy and drift detection delay. A validation on synthetic and real-life logs shows that the method accurately detects typical change patterns and scales up to the extent it is applicable for online drift detection.

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ID Code: 83013
Item Type: Conference Paper
Refereed: Yes
Keywords: process mining, concept drift, statistical test, chi square, business process management
ISBN: 9783319230634
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600) > Information Systems Development Methodologies (080608)
Divisions: Past > Schools > Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2015 Springer International Publishing Switzerland
Deposited On: 01 Apr 2015 00:50
Last Modified: 13 Sep 2016 03:39

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