Process Mining Techniques in Business Environments Theoretical Aspec
After a brief presentation of the state of the art of process-mining techniques, Andrea Burratin proposes different scenarios for the deployment of process-mining projects, and in particular a characterization of companies in terms of their process awaren
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Andrea Burattin
Process Mining Techniques in Business Environments Theoretical Aspects, Algorithms, Techniques and Open Challenges in Process Mining
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Lecture Notes in Business Information Processing Series Editors Wil van der Aalst Eindhoven Technical University, Eindhoven, The Netherlands John Mylopoulos University of Trento, Povo, Italy Michael Rosemann Queensland University of Technology, Brisbane, QLD, Australia Michael J. Shaw University of Illinois, Urbana-Champaign, IL, USA Clemens Szyperski Microsoft Research, Redmond, WA, USA
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More information about this series at http://www.springer.com/series/7911
Andrea Burattin
Process Mining Techniques in Business Environments Theoretical Aspects, Algorithms, Techniques and Open Challenges in Process Mining
123
Andrea Burattin University of Innsbruck Innsbruck Austria
ISSN 1865-1348 ISSN 1865-1356 (electronic) Lecture Notes in Business Information Processing ISBN 978-3-319-17481-5 ISBN 978-3-319-17482-2 (eBook) DOI 10.1007/978-3-319-17482-2 Library of Congress Control Number: 2015938082 Springer Cham Heidelberg New York Dordrecht London © Springer International Publishing Switzerland 2015 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper Springer International Publishing AG Switzerland is part of Springer Science+Business Media (www.springer.com)
Preface
This book encompasses a revised version of the Ph.D. dissertation, written by the author, at the Mathematics Department of the University of Padua (Italy), and at the Computer Science Department of the University of Bologna (Italy). In 2014, the dissertation won the “Best Process Mining Dissertation Award”, assigned by the IEEE Task Force on Process Mining to the most outstanding Ph.D. thesis, discussed between 2012 and 2013, focused on the area of business process intelligence. The increasing availability of storage and computing capability, combined with the advent of new “smart” devices, represents the fundamental basis of the so-
