Mining business process stages from event logs

Nguyen, Hoang Huy, Dumas, Marlon, Hofstedeter, Arthur H.M., La Rosa, Marcello, & Maggi, Fabrizio Maria (2017) Mining business process stages from event logs. In 29th International Conference on Advanced Information Systems Engineering (CAiSE2017), 12-16 June 2017, Essen, Germany. (In Press)

Abstract

Process mining is a family of techniques to analyze business processes based on event logs recorded by their supporting information systems. Two recurrent bottlenecks of existing process mining techniques when confronted with real-life event logs are scalability and interpretability of the outputs. A common approach to tackle these limitations is to decompose the process under analysis into a set of stages, such that each stage can be mined separately. However, existing techniques for automated discovery of stages from event logs produce decompositions that are very different from those that domain experts would produce manually. This paper proposes a technique that, given an event log, discovers a stage decomposition that maximizes a measure of modularity borrowed from the field of social network analysis. An empirical evaluation on real-life event logs shows that the produced decompositions more closely approximate manual decompositions than existing techniques.

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ID Code: 102251
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: process mining, staged process, stages, event logs, clustering
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)
Divisions: Past > Schools > Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: 2017 [Please consult the author]
Deposited On: 08 Dec 2016 00:43
Last Modified: 05 Mar 2017 23:08

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