Overview
- Revised version of a PhD dissertation in process mining
- Won the "Best Process Mining Dissertation Award", assigned by the IEEE Task Force on Process Mining
- Deals with conformance checking, one of the main areas of process mining
- Includes supplementary material: sn.pub/extras
Part of the book series: Lecture Notes in Business Information Processing (LNBIP, volume 270)
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Table of contents (19 chapters)
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Front Matter
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Conformance Checking in Process Mining
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Front Matter
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Decomposition in Conformance Checking
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Front Matter
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Keywords
About this book
Process mining techniques can be used to discover, analyze and improve real processes, by extracting models from observed behavior. The aim of this book is conformance checking, one of the main areas of process mining. In conformance checking, existing process models are compared with actual observations of the process in order to assess their quality. Conformance checking techniques are a way to visualize the differences between assumed process represented in the model and the real process in the event log, pinpointing possible problems to address, and the business process management results that rely on these models.
This book combines both application and research perspectives. It provides concrete use cases that illustrate the problems addressed by the techniques in the book, but at the same time, it contains complete conceptualization and formalization of the problem and the techniques, and through evaluations on the quality and the performance of the proposed techniques. Hence, this book brings the opportunity for business analysts willing to improve their organization processes, and also data scientists interested on the topic of process-oriented data science.
Authors and Affiliations
Bibliographic Information
Book Title: Conformance Checking and Diagnosis in Process Mining
Book Subtitle: Comparing Observed and Modeled Processes
Authors: Jorge Munoz-Gama
Series Title: Lecture Notes in Business Information Processing
DOI: https://doi.org/10.1007/978-3-319-49451-7
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing AG 2016
Softcover ISBN: 978-3-319-49450-0Published: 25 November 2016
eBook ISBN: 978-3-319-49451-7Published: 22 November 2016
Series ISSN: 1865-1348
Series E-ISSN: 1865-1356
Edition Number: 1
Number of Pages: XIV, 202
Number of Illustrations: 90 b/w illustrations
Topics: Computer Appl. in Administrative Data Processing, Business Process Management, Data Mining and Knowledge Discovery