Discovering Work Prioritisation Patterns from Event Logs

Suriadi, Suriadi, Wynn, Moe T., Xu, Jingxin, van der Aalst, Wil M.P., & ter Hofstede, Arthur H.M. (2017) Discovering Work Prioritisation Patterns from Event Logs. Decision Support Systems. (In Press)

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•A new approach to detect resources work priorisation from event log is proposed.

•A novel concept of queue distance is introduced to predict prioritisation pattern.

•It has been implemented as a plug-in tool within an open-source environment.

•It has been evaluated using a real log from an Australian insurance organisation

•The evaluation of this approach within the organisation reveals useful insights.


Business process improvement initiatives typically employ various process analysis techniques, including evidence-based analysis techniques such as process mining, to identify new ways to streamline current business processes. While plenty of process mining techniques have been proposed to extract insights about the way in which activities within processes are conducted, techniques to understand resource behaviour are limited. At the same time, an understanding of resources behaviour is critical to enable intelligent and effective resource management - an important factor which can significantly impact overall process performance. The presence of detailed records kept by today’s organisations, including data about who, how, what, and when various activities were carried out by resources, open up the possibility for real behaviours of resources to be studied. This paper proposes an approach to analyse one aspect of resource behaviour: the manner in which a resource prioritises his/her work. The proposed approach has been formalised, implemented, and evaluated using a number of synthetic and real datasets.

Keywords: Resource behaviour mining; Queueing; Process mining

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ID Code: 103556
Item Type: Journal Article
Refereed: Yes
DOI: 10.1016/j.dss.2017.02.002
ISSN: 01679236
Divisions: Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Elsevier
Deposited On: 09 Feb 2017 05:53
Last Modified: 14 Feb 2017 17:11

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