Metrics For Process Models: Empirical Foundations Of Verification, Error Prediction, And Guidelines For Correctness (lecture Notes In Business Information Processing)

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Business process modeling plays an important role in the management of business processes. As valuable design artifacts, business process models are subject to quality considerations. The absence of formal errors such as deadlocks is of paramount importance for the subsequent implementation of the process. In his book Jan Mendling develops a framework for the detection of formal errors in business process models and the prediction of error probability based on quality attributes of these models (metrics). He presents a precise description of Event-driven Process Chains (EPCs), their control-flow semantics and a suitable correctness criterion called EPC soundness.

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Lecture Notes in Business Information Processing Series Editors Wil van der Aalst Eindhoven Technical University, The Netherlands John Mylopoulos University of Trento, Italy Norman M. Sadeh Carnegie Mellon University, Pittsburgh, PA, USA Michael J. Shaw University of Illinois, Urbana-Champaign, IL, USA Clemens Szyperski Microsoft Research, Redmond, WA, USA 6 Jan Mendling Metrics for Process Models Empirical Foundations of Verification, Error Prediction, and Guidelines for Correctness 13 Author Jan Mendling Humboldt-Universität zu Berlin Institut für Wirtschaftsinformatik Spandauer Str. 1, 10178 Berlin, Germany E-mail: [email protected] Library of Congress Control Number: 2008938155 ACM Computing Classification (1998): H.4, J.1, D.2 ISSN ISBN-10 ISBN-13 1865-1348 3-540-89223-0 Springer Berlin Heidelberg New York 978-3-540-89223-6 Springer Berlin Heidelberg New York 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, re-use of illustrations, recitation, broadcasting, reproduction on microfilms 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. Springer is a part of Springer Science+Business Media springer.com © Springer-Verlag Berlin Heidelberg 2008 Printed in Germany Typesetting: Camera-ready by author, data conversion by Markus Richter, Heidelberg Printed on acid-free paper SPIN: 12540204 06/3180 543210 To Leni and to my family Preface Business process modeling plays an important role in the management of business processes. As valuable design artifacts, business process models are subject to quality considerations. The absence of formal errors such as deadlocks is of paramount importance for the subsequent implementation of the process. This book develops a framework for the detection of formal errors in business process models and for the prediction of error probability based on quality attributes of these models (metrics). We focus on Event-driven Process Chains (EPCs), a widely used business process modeling language due to its extensive tool support. The advantage of this focus is firstly that the results of this book can be directly translated into process modeling practice. Secondly, there is a large empirical basis of models. By utilizing this large stock of EPC model collections, we aim to bring forth general insights into the connection between process model metrics and error probability. In order to validate such a connection, we first need to establish an understanding of which model attributes are likely connected with error probability. Furthermore, we must formally define an appropriate notion of correctness that answers th
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