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Conformance Checking And Diagnosis In Process Mining Comparing Observed And Modeled Processes 1st Edition Jorge Munozgama Auth

  • SKU: BELL-5737144
Conformance Checking And Diagnosis In Process Mining Comparing Observed And Modeled Processes 1st Edition Jorge Munozgama Auth
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Conformance Checking And Diagnosis In Process Mining Comparing Observed And Modeled Processes 1st Edition Jorge Munozgama Auth instant download after payment.

Publisher: Springer International Publishing
File Extension: PDF
File size: 7.1 MB
Pages: 201
Author: Jorge Munoz-Gama (auth.)
ISBN: 9783319494500, 9783319494517, 3319494503, 3319494511
Language: English
Year: 2016
Edition: 1

Product desciption

Conformance Checking And Diagnosis In Process Mining Comparing Observed And Modeled Processes 1st Edition Jorge Munozgama Auth by Jorge Munoz-gama (auth.) 9783319494500, 9783319494517, 3319494503, 3319494511 instant download after payment.

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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