Multimedia Data Mining and Analytics Disruptive Innovation
This authoritative text/reference provides fresh insights into the cutting edge of multimedia data mining, reflecting how the research focus has shifted towards networked social communities, mobile devices and sensors.Presenting a detailed exploration int
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timedia Data Mining and Analytics Disruptive Innovation
Multimedia Data Mining and Analytics
Aaron K. Baughman Jiang Gao Jia-Yu Pan Valery A. Petrushin •
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Editors
Multimedia Data Mining and Analytics Disruptive Innovation
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Editors Aaron K. Baughman IBM Corp. Durham, NC USA
Jia-Yu Pan Google Inc. Mountain View, CA USA
Jiang Gao Nokia Inc. Sunnyvale, CA USA
Valery A. Petrushin 4i, Inc. Carlsbad, CA USA
ISBN 978-3-319-14997-4 DOI 10.1007/978-3-319-14998-1
ISBN 978-3-319-14998-1
(eBook)
Library of Congress Control Number: 2014959196 Springer Cham Heidelberg New York Dordrecht London © Springer International Publishing Switzerland 2015 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 publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper Springer International Publishing AG Switzerland is part of Springer Science+Business Media (www.springer.com)
Preface
In recent years, disruptive developments in computing technology, such as largescale and mobile computing, has accelerated the growth in volume, velocity, and variety of multimedia data while enabling tantalizing analytical processing potential. During the last decade, multimedia data mining research extended its scope to cover more data modalities and shifted its focus from analysis of data of one modality to multi-modal data, from content-base search to concept-base search, and from corporate data to social networked communities data. Ubiquity of advanced computing devices such as smart phones, tablets, e-book readers, networked gaming platforms, which serve both as data producers and ideal personalized delivery tools, brought a wealth of new data types including geographical aware data, and personal behavioral, preference and sentiment data. Developments in networked sensor technology allow enriched behavioral personal data that include physiological and environmental data that can be implemented to build deep, intrinsic, and robust models. This book reflects on the major focus shifts in multimedia data mining research and applicatio
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