White-box prediction of process performance indicators via flow analysis

Verenich, Ilya, Nguyen, Hoang, La Rosa, Marcello, & Dumas, Marlon (2017) White-box prediction of process performance indicators via flow analysis. In International Conference on Software and System Processes (ICSSP'17), 5-7 July 2017, Paris, France.

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Abstract

Predictive business process monitoring methods exploit historical process execution logs to provide predictions about running instances of a process, which enable process workers and managers to preempt performance issues or compliance violations. A number of approaches have been proposed to predict quantitative process performance indicators, such as remaining cycle time, cost, or probability of deadline violation. However, these approaches adopt a black-box approach, insofar as they predict a single scalar value without decomposing this prediction into more elementary components. In this paper, we propose a white-box approach to predict performance indicators of running process instances. The key idea is to first predict the performance indicator at the level of activities, and then to aggregate these predictions at the level of a process instance by means of flow analysis techniques. The paper specifically develops this idea in the context of predicting the remaining cycle time of ongoing process instances. The proposed approach has been evaluated on four real-life event logs and compared against several baselines.

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ID Code: 106395
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Process Mining, Predictive Process Monitoring, Flow analysis
DOI: 10.1145/3084100.3084110
ISBN: 781450352703
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) > Decision Support and Group Support Systems (080605)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600) > Information Engineering and Theory (080607)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Current > Schools > School of Information Systems
Funding:
Copyright Owner: 2017 ACM
Deposited On: 02 May 2017 05:16
Last Modified: 09 May 2017 17:06

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