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Conformance Checking and Diagnosis in Process Mining
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Conformance Checking and Diagnosis in Process Mining : Comparing Observed and Modeled Processes

1st ed. 2016

Book Details

Format Paperback / Softback
ISBN-10 3319494503
ISBN-13 9783319494500
Edition 1st ed. 2016
Publisher Springer International Publishing AG
Imprint Springer International Publishing AG
Country of Manufacture CH
Country of Publication GB
Publication Date Nov 25th, 2016
Print length 202 Pages
Weight 348 grams
Dimensions 15.50 x 23.40 x 1.80 cms
Ksh 8,100.00
Werezi Extended Catalogue 0 in stock

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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.

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.


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