8. Algorithms for Data Streams

  1. Amiya Nayak B.Math., Ph.D. Adjunct Research Professor Associate Editor Full Professor2 and
  2. Ivan Stojmenović Ph.D. Chair Professor founder editor-in-chief2,3
  1. Camil Demetrescu and
  2. Irene Finocchi

Published Online: 1 MAR 2007

DOI: 10.1002/9780470175668.ch8

Handbook of Applied Algorithms: Solving Scientific, Engineering and Practical Problems

Handbook of Applied Algorithms: Solving Scientific, Engineering and Practical Problems

How to Cite

Demetrescu, C. and Finocchi, I. (2007) Algorithms for Data Streams, in Handbook of Applied Algorithms: Solving Scientific, Engineering and Practical Problems (eds A. Nayak and I. Stojmenović), John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9780470175668.ch8

Editor Information

  1. 2

    SITE, University of Ottawa, 800 King Edward Ave., Ottawa, ON K1N 6N5, Canada

  2. 3

    EECE, University of Birmingham, UK

Author Information

  1. Department of Computer and Systems Science, University of Rome “La Sapienza”, Via Salaria 113, 00198 Rome, Italy

Publication History

  1. Published Online: 1 MAR 2007
  2. Published Print: 14 FEB 2008

ISBN Information

Print ISBN: 9780470044926

Online ISBN: 9780470175668

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Keywords:

  • data stream algorithms;
  • algorithm design techniques;
  • PRAM algorithms and PRAM model

Summary

Data stream processing has recently gained increasing popularity as an effective paradigm for processing massive data sets. A wide range of applications in computational sciences generate huge and rapidly changing data streams that need to be continuously monitored in order to support exploratory analyses and to detect correlations, rare events, fraud, intrusion, and unusual or anomalous activities. Relevant examples include monitoring network traffic, online auctions, transaction logs, telephone call records, automated bank machine operations, and atmospheric and astronomical events. Due to the high sequential access rates of modern disks, streaming algorithms can also be effectively deployed for processing massive files on secondary storage, providing new insights into the solution of several computational problems in external memory. Streaming models constrain algorithms to access the input data in one or few sequential passes, using only a small amount of working memory and processing each input item quickly. Solving computational problems under these restrictions poses several algorithmic challenges.