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2017 | OriginalPaper | Chapter

Mining Business Process Stages from Event Logs

Authors : Hoang Nguyen, Marlon Dumas, Arthur H. M. ter Hofstede, Marcello La Rosa, Fabrizio Maria Maggi

Published in: Advanced Information Systems Engineering

Publisher: Springer International Publishing

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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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previous chapter An Approach for Incorporating Expert Knowledge in Trace Clustering
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Footnotes
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Metadata
Title
Mining Business Process Stages from Event Logs
Authors
Hoang Nguyen
Marlon Dumas
Arthur H. M. ter Hofstede
Marcello La Rosa
Fabrizio Maria Maggi
Copyright Year
2017
Book
Advanced Information Systems Engineering

Print ISBN: 978-3-319-59535-1

Electronic ISBN: 978-3-319-59536-8

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-59536-8_36

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