Overview
- Received the BPM 2020 Dissertation Award for an outstanding thesis in the field of business process management
- Introduces the concept of “process realism” by combining methods from process discovery and process analytics
- Includes empirical case studies to show how the developed framework works in practice
Part of the book series: Lecture Notes in Business Information Processing (LNBIP, volume 412)
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Table of contents (10 chapters)
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Front Matter
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Introduction
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Front Matter
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Process Analytics
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Front Matter
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Conclusions
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Front Matter
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Back Matter
Keywords
About this book
This book is a revised version of the PhD dissertation written by the author at Hasselt University in Belgium.
This dissertation introduces the concept of process realism. Process realism is approached from two perspectives in this dissertation. First, quality dimensions and measures for process discovery are analyzed on a large scale and compared with each other on the basis of empirical experiments. It is shown that there are important differences between the different quality measures in terms of feasibility, validity and sensitivity. Moreover, the role and meaning of the generalization dimension is unclear. Second, process realism is also tackled from a data point of view. By developing a transparent and extensible tool-set, a framework is offered to analyze process data from different perspectives. From both perspectives, recommendations are made for future research, and a call is made to give the process realism mindset a central place within process mining analyses.In 2020, the PhD dissertation won the “BPM Dissertation Award”, granted to outstanding PhD theses in the field of Business Process Management.
Authors and Affiliations
Bibliographic Information
Book Title: Unearthing the Real Process Behind the Event Data
Book Subtitle: The Case for Increased Process Realism
Authors: Gert Janssenswillen
Series Title: Lecture Notes in Business Information Processing
DOI: https://doi.org/10.1007/978-3-030-70733-0
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2021
Softcover ISBN: 978-3-030-70732-3Published: 08 April 2021
eBook ISBN: 978-3-030-70733-0Published: 07 April 2021
Series ISSN: 1865-1348
Series E-ISSN: 1865-1356
Edition Number: 1
Number of Pages: XVI, 283
Number of Illustrations: 39 b/w illustrations, 58 illustrations in colour