Data Streams

Models and Algorithms

  • Charu C. Aggarwal

Part of the Advances in Database Systems book series (ADBS, volume 31)

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Charu C. Aggarwal
    Pages 1-8
  3. Charu C. Aggarwal, Jiawei Han, Jianyong Wang, Philip S. Yu
    Pages 9-38
  4. Mohamed Medhat Gaber, Arkady Zaslavsky, Shonali Krishnaswamy
    Pages 39-59
  5. Ruoming Jin, Gagan Agrawal
    Pages 61-84
  6. Jiawei Han, Y. Dora Cai, Yixin Chen, Guozhu Dong, Jian Pei, Benjamin W. Wah et al.
    Pages 103-125
  7. Brian Babcock, Mayur Datar, Rajeev Motwani
    Pages 127-147
  8. Mayur Datar, Rajeev Motwani
    Pages 149-167
  9. Charu C. Aggarwal, Philip S. Yu
    Pages 169-207
  10. Junyi Xie, Jun Yang
    Pages 209-236
  11. Ahmet Bulut, Ambuj K. Singh
    Pages 237-259
  12. Spiros Papadimitriou, Jimeng Sun, Christos Faloutsos
    Pages 261-288
  13. Srinivasan Parthasarathy, Amol Ghoting, Matthew Eric Otey
    Pages 289-307
  14. Kanishka Bhaduri, Kamalika Das, Krishnamoorthy Sivakumar, Hillol Kargupta, Ran Wolff, Rong Chen
    Pages 309-331
  15. Sharmila Subramaniam, Dimitrios Gunopulos
    Pages 333-352
  16. Back Matter
    Pages 353-354

About this book

Introduction

In recent years, the progress in hardware technology has made it possible for organizations to store and record large streams of transactional data.  Such data sets which continuously and rapidly grow over time are referred to as data streams.

Data Streams: Models and Algorithms primarily discusses issues related to the mining aspects of data streams rather than the database management aspect of streams. This volume covers mining aspects of data streams in a comprehensive style. Each contributed chapter, from a variety of well known researchers in the data mining field, contains a survey on the topic, the key ideas in the field from that particular topic, and future research directions.

Data Streams: Models and Algorithms is intended for a professional audience composed of researchers and practitioners in industry. This book is also appropriate for graduate-level students in computer science.

Charu C. Aggarwal obtained his B.Tech in Computer Science from IIT Kanpur in 1993 and Ph.D. from MIT in 1996. He has been a Research Staff Member at IBM since then, and has published over 90 papers in major conferences and journals in the database and data mining field. He has applied for, or been granted, over 50 US and International patents, and has twice been designated Master Inventor at IBM for the commercial value of his patents. He has been granted 14 invention achievement awards by IBM for his patents. His work on real time bio-terrorist threat detection in data streams won the IBM Epispire award for environmental excellence in 2003. He has served on the program committee of most major database conferences, and was program chair for the Data Mining and Knowledge Discovery Workshop, 2003, and a program vice-chair for the SIAM Conference on Data Mining, 2007. He is an associate editor of the IEEE Transactions on Data Engineering and an action editor of the Data Mining and Knowledge Discovery Journal. He is a senior member of the IEEE.

Keywords

algorithm algorithms data data streams database frequent pattern mining in streams models organization sensor networks stream classification stream clustering stream mining stream summarization

Editors and affiliations

  • Charu C. Aggarwal
    • 1
  1. 1.IBMThomas J. Watson Research CenterHawthorne

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-387-47534-9
  • Copyright Information Springer Science+Business Media, LLC 2007
  • Publisher Name Springer, Boston, MA
  • eBook Packages Computer Science
  • Print ISBN 978-0-387-28759-1
  • Online ISBN 978-0-387-47534-9
  • Series Print ISSN 1386-2944
  • About this book