Scalable Uncertainty Management First International Conference,
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Subseries of Lecture Notes in Computer Science
4772
Henri Prade V.S. Subrahmanian (Eds.)
Scalable Uncertainty Management First International Conference, SUM 2007 Washington, DC, USA, October 10-12, 2007 Proceedings
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Series Editors Jaime G. Carbonell, Carnegie Mellon University, Pittsburgh, PA, USA Jörg Siekmann, University of Saarland, Saarbrücken, Germany Volume Editors Henri Prade Université Paul Sabatier IRIT 118 route de Narbonne, 31062 Toulouse Cedex, France E-mail: [email protected] V.S. Subrahmanian University of Maryland Department of Computer Science and UMIACS AV Williams Building, College Park MD 20742, USA E-mail: [email protected]
Library of Congress Control Number: 2007936370
CR Subject Classification (1998): I.2, F.4.1 LNCS Sublibrary: SL 7 – Artificial Intelligence ISSN ISBN-10 ISBN-13
0302-9743 3-540-75407-5 Springer Berlin Heidelberg New York 978-3-540-75407-7 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 2007 Printed in Germany Typesetting: Camera-ready by author, data conversion by Scientific Publishing Services, Chennai, India Printed on acid-free paper SPIN: 12168651 06/3180 543210
Preface
Although there has been extensive work on the management of uncertainty, relatively little work has been done on efficient and scalable methods to manage the uncertainty that arises in real-world applications. While the artificial intelligence community has studied mathematical models of uncertainty and developed many useful applications, the database community has focused on building uncertainty tools directly into underlying database infrastructure. The goal of the Scalable Uncertainty Management (SUM) conference is to take the first steps toward bringing together artificial intelligence researchers, database researchers, and practitioners to see how the best theoretical techniques can be made to scale up to the needs of large-scale applications. SUM 2007 used a rigorous refereeing procedure to review all papers. Papers by the PC chairs were reviewed by a subcommittee unknown to the PC chairs. After this review process, we accepted a total of 20 papers which are presented in this volume. October 2007
Henri Prade V.S. Subrahmanian
Organization
SUM 2007 was organized by the University of Maryland Institute for Advanced Computer Studies (UMIACS), University of Marlyand College Park.
Executive Committee Conference General Chair Program Chai
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