Data Stream Management Processing High-Speed Data Streams
We live in the era of “Big Data”: Petabytes of digital information are generated daily, and need to be processed and analyzed for interesting patterns and trends. Besides volume, a defining characteristic of Big Data is its velocity; that is, data is inst
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Minos Garofalakis r Johannes Gehrke Rajeev Rastogi
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Editors
Data Stream Management Processing High-Speed Data Streams
Editors Minos Garofalakis School of Electrical and Computer Engineering Technical University of Crete Chania, Greece
Rajeev Rastogi Amazon India Bangalore, India
Johannes Gehrke Microsoft Corporation Redmond, WA, USA
ISSN 2197-9723 Data-Centric Systems and Applications ISBN 978-3-540-28607-3 DOI 10.1007/978-3-540-28608-0
ISSN 2197-974X (electronic) ISBN 978-3-540-28608-0 (eBook)
Library of Congress Control Number: 2016946344 Springer Heidelberg New York Dordrecht London © Springer-Verlag Berlin Heidelberg 2016 The fourth chapter in part 4 is published with kind permission of © 2004 Association for Computing Machinery, Inc.. All rights reserved. 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 is part of Springer Science+Business Media (www.springer.com)
Contents
Data Stream Management: A Brave New World . . . . . . . . . . . . . Minos Garofalakis, Johannes Gehrke, and Rajeev Rastogi Part I
1
Foundations and Basic Stream Synopses
Data-Stream Sampling: Basic Techniques and Results . . . . . . . . . . Peter J. Haas
13
Quantiles and Equi-depth Histograms over Streams . . . . . . . . . . . Michael B. Greenwald and Sanjeev Khanna
45
Join Sizes, Frequency Moments, and Applications . . . . . . . . . . . . Graham Cormode and Minos Garofalakis
87
Top-k Frequent Item Maintenance over Streams . . . . . . . . . . . . . Moses Charikar
103
Distinct-Values Estimation over Data Streams . . . . . . . . . . . . . . Phillip B. Gibbons
121
The Sliding-Window Computation Model and Results . . . . . . . . . . Mayur Datar and Rajeev Motwani
149
Part II
Mining Data Streams
Clustering Data Streams . . . . . . . . . . . . . . . . . . . . . . . . . . Sudipto Guha and Nina Mishra
169
Mining Decision Trees from Streams . . . . . . . . . . . . . .
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