Rule Based Systems for Big Data A Machine Learning Approach
The ideas introduced in this book explore the relationships among rule based systems, machine learning and big data. Rule based systems are seen as a special type of expert systems, which can be built by using expert knowledge or learning from real data.
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Han Liu Alexander Gegov Mihaela Cocea
Rule Based Systems for Big Data A Machine Learning Approach
Studies in Big Data Volume 13
Series editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland e-mail: [email protected]
About this Series The series “Studies in Big Data” (SBD) publishes new developments and advances in the various areas of Big Data- quickly and with a high quality. The intent is to cover the theory, research, development, and applications of Big Data, as embedded in the fields of engineering, computer science, physics, economics and life sciences. The books of the series refer to the analysis and understanding of large, complex, and/or distributed data sets generated from recent digital sources coming from sensors or other physical instruments as well as simulations, crowd sourcing, social networks or other internet transactions, such as emails or video click streams and other. The series contains monographs, lecture notes and edited volumes in Big Data spanning the areas of computational intelligence incl. neural networks, evolutionary computation, soft computing, fuzzy systems, as well as artificial intelligence, data mining, modern statistics and Operations research, as well as selforganizing systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output.
More information about this series at http://www.springer.com/series/11970
Han Liu Alexander Gegov Mihaela Cocea •
Rule Based Systems for Big Data A Machine Learning Approach
123
Mihaela Cocea School of Computing University of Portsmouth Portsmouth UK
Han Liu School of Computing University of Portsmouth Portsmouth UK Alexander Gegov School of Computing University of Portsmouth Portsmouth UK
ISSN 2197-6503 Studies in Big Data ISBN 978-3-319-23695-7 DOI 10.1007/978-3-319-23696-4
ISSN 2197-6511
(electronic)
ISBN 978-3-319-23696-4
(eBook)
Library of Congress Control Number: 2015948735 Springer Cham Heidelberg New York Dordrecht London © Springer International Publishing Switzerland 2016 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, 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. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of public
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