Knowledge Processing with Interval and Soft Computing
Massive datasets, made available today by modern technologies, present a significant challenge to scientists who need to effectively and efficiently extract relevant knowledge and information. Due to their ability to model uncertainty, interval and soft c
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Chenyi Hu Ralph Baker Kearfott Andr´e de Korvin Vladik Kreinovich •
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Editors
Knowledge Processing with Interval and Soft Computing
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Editors Chenyi Hu Computer Science Department University of Central Arkansas Conway, Arkansas, USA
Ralph Baker Kearfott Mathematics Department University of Louisiana, USA
Andr´e de Korvin Computer and Mathematical Sciences Department University of Houston-Downtown, USA
AI&KP ISSN 1610-3947 ISBN: 978-1-84800-325-5 DOI: 10.1007/978-1-84800-326-2
Vladik Kreinovich Computer Science Department University of Texas, USA
e-ISBN: 978-1-84800-326-2
British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Control Number: 2008933360 c Springer-Verlag London Limited 2008 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms of licenses issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. The use of 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 laws and regulations and therefore free for general use. The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made. Printed on acid-free paper Springer Science+Business Media springer.com
Preface
Modern technologies have collected massive datasets from observations, experiments, and scientific simulation. Although progress has been made, it still remains a challenge to effectively and efficiently discover knowledge from such massive datasets. This is mainly because of the following two features. One is that the size and number of attributes (dimensions) of some datasets can be unmanageable. The other is that, due to the dynamic nature of the real world, changes and uncertainties are characteristics of datasets. A significant change in scientists’ ability to analyze data to obtain a better understanding of natural phenomena will be enabled by (i) new ways to manage massive amounts of data from observations and scientific simulation, (ii) integration of powerful analysis tools directly into the database, ··· Final report of the International Science 2020 Group, Microsoft, 2006 In contrast to classical point methods for arranging and processing data, in this book, we investigate strategies for knowledge processing with interval and soft computing. Knowledge processing with interval methods has intrin
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