Telematica

Process Mining

Wil M.P. van der Aalst

Process Mining
Discovery, Conformance and Enhancement of Business Processes

Wil M.P. van der Aalst Department Mathematics & Computer Science Eindhoven University of Technology Den Dolech 2 5612 AZ Eindhoven The Netherlands w.m.p.v.d.aalst@tue.nl

ISBN 978-3-642-19344-6 e-ISBN 978-3-642-19345-3 DOI 10.1007/978-3-642-19345-3 Springer HeidelbergDordrecht London New York
Library of Congress Control Number: 2011926240 ACM Computing Classification (1998): H.4.1, H.2.8, I.2.6, F.3.2, D.2.2, J.1 © Springer-Verlag Berlin Heidelberg 2011 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation,broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of generaldescriptive names, registered names, trademarks, 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. Cover design: deblik Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Thanks to Karin for understanding thatscience is more rewarding than running errands Thanks to all people that contributed to ProM; the fruits of their efforts demonstrate that sharing a common goal is more meaningful than “cashing in the next publon”1 In remembrance of Gerry Straatman-Beelen (1932–2010)

1 publon

= smallest publishable unit

Preface

Process mining provides a new means to improve processes in a variety ofapplication domains. There are two main drivers for this new technology. On the one hand, more and more events are being recorded thus providing detailed information about the history of processes. Despite the omnipresence of event data, most organizations diagnose problems based on fiction rather than facts. On the other hand, vendors of Business Process Management (BPM) and Business Intelligence(BI) software have been promising miracles. Although BPM and BI technologies received lots of attention, they did not live up to the expectations raised by academics, consultants, and software vendors. Process mining is an emerging discipline providing comprehensive sets of tools to provide fact-based insights and to support process improvements. This new discipline builds on process model-drivenapproaches and data mining. However, process mining is much more than an amalgamation of existing approaches. For example, existing data mining techniques are too data-centric to provide a comprehensive understanding of the end-to-end processes in an organization. BI tools focus on simple dashboards and reporting rather than clear-cut business process insights. BPM suites heavily rely on expertsmodeling idealized to-be processes and do not help the stakeholders to understand the as-is processes. This book presents a range of process mining techniques that help organizations to uncover their actual business processes. Process mining is not limited to process discovery. By tightly coupling event data and process models, it is possible to check conformance, detect deviations, predictdelays, support decision making, and recommend process redesigns. Process mining breathes life into otherwise static process models and puts today’s massive data volumes in a process context. Hence, managements trends related to process improvement (e.g., Six Sigma, TQM, CPI, and CPM) and compliance (SOX, BAM, etc.) can benefit from process mining. Process mining, as described in this book, emerged in...