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Elements for Tailoring a BPM Maturity Model to Simplify its Use Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Toward a New Generation of Log Pre-processing Methods for Process Mining

Authors : Paolo Ceravolo, Ernesto Damiani, Mohammadsadegh Torabi, Sylvio Barbon Jr.

Published in: Business Process Management Forum

Publisher: Springer International Publishing

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Abstract

Real-life processes are typically less structured and more complex than expected by stakeholders. For this reason, process discovery techniques often deliver models less understandable and useful than expected. In order to address this issue, we propose a method based on statistical inference for pre-processing event logs. We measure the distance between different segments of the event log, computing the probability distribution of observing activities in specific positions. Because segments are generated based on time-domain, business rules or business management system properties, we get a characterisation of these segments in terms of both business and process aspects. We demonstrate the applicability of this approach by developing a case study with real-life event logs and showing that our method is offering interesting properties in term of computational complexity.

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previous chapter A Template for Categorizing Business Processes in Empirical Research
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Footnotes
1
Some works, such as for instance [22], define as “Clustering” the identification of similar activities, this is also a pre-processing task relevant to our discussion, however in this paper we are using “Clustering” for referring uniquely to the process of segmenting event logs.
 
2
The Python implementation of the algorithms adopted to implement and test our method is available at http://​www.​uel.​br/​grupo-pesquisa/​remid/​wp-content/​uploads/​LightPMClusterin​g.​rar.
 
4
The edit distance between two strings is the minimum number of operations required to transform one string into the other.
 
5
Clearly, by generating segments the information on the control-flow encoded in matrices is aggregated using a compensative approach that can bias the comparisons. We plan to address this problem in future studies by using intra- and inter-segment similarity metrics.
 
6
Note that, when we do not reject \(H_{0}\), it does not mean that \(H_{0}\) is true. It means that the sample data have failed to provide sufficient evidence to cast serious doubt about the truthfulness of \(H_{0}\).
 
7
The rates provided are 3 “Neither agree nor disagree”, 4 “Agree”, and 4 “Agree”.
 
8
The rates provided are 1 “Strongly disagree”, 2 “Disagree”, and 2 “Disagree”.
 
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Metadata
Title
Toward a New Generation of Log Pre-processing Methods for Process Mining
Authors
Paolo Ceravolo
Ernesto Damiani
Mohammadsadegh Torabi
Sylvio Barbon Jr.
Copyright Year
2017
Book
Business Process Management Forum

Print ISBN: 978-3-319-65014-2

Electronic ISBN: 978-3-319-65015-9

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-65015-9_4