Metrics for Process Models Empirical Foundations of Verification, Er
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 importan
- PDF / 4,826,799 Bytes
- 205 Pages / 429.725 x 659.895 pts Page_size
- 4 Downloads / 199 Views
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 the question of whether or not a model has a formal error. As a prerequisite to answering this question, we must define the operational semantics of the process modeling language formally. Contributions This book presents a precise description of EPCs, their control-flow semantics and a suitable correctness criterion called EPC soundness. Furthermore, we identify theoretical arguments on why structural metrics should be
Data Loading...
