ProcessProfiler3D: A visualisation framework for log-based process performance comparison

Wynn, Moe T., Poppe, Erik, Xu, Jingxin, ter Hofstede, Arthur H.M., Brown, Ross A., Pini, Azzurra, & van der Aalst, Wil M.P. (2017) ProcessProfiler3D: A visualisation framework for log-based process performance comparison. Decision Support Systems. (In Press)

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Abstract

An organisation can significantly improve its performance by observing how their business operations are currently being carried out. A great way to derive evidence-based process improvement insights is to compare the behaviour and performance of processes for different process cohorts by utilising the information recorded in event logs. A process cohort is a coherent group of process instances that has one or more shared characteristics. Such process performance comparisons can highlight positive or negative variations that can be evident in a particular cohort, thus enabling a tailored approach to process improvement. Although existing process mining techniques can be used to calculate various statistics from event logs for performance analysis, most techniques calculate and display the statistics for each cohort separately. Furthermore, the numerical statistics and simple visualisations may not be intuitive enough to allow users to compare the performance of various cohorts efficiently and effectively. We developed a novel visualisation framework for log-based process performance comparison to address these issues. It enables analysts to quickly identify the performance differences between cohorts. The framework supports the selection of cohorts and a three-dimensional visualisation to compare the cohorts using a variety of performance metrics. The approach has been implemented as a set of plug-ins within the open source process mining framework ProM and has been evaluated using two real-life data sets from the insurance domain to assess the usefulness of such a tool. This paper also derives a set of design principles from our approach which provide guidance for the development of new approaches to process cohort performance comparison.

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ID Code: 105525
Item Type: Journal Article
Refereed: Yes
Additional URLs:
Keywords: Process Analysis, Process Mining, Performance Analysis, Visualisation
DOI: 10.1016/j.dss.2017.04.004
ISSN: 1873-5797
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600) > Computer-Human Interaction (080602)
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 Electrical Engineering & Computer Science
Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 07 Apr 2017 03:44
Last Modified: 16 May 2017 01:47

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