Data Streams and Data Synopses for Massive Data Sets (Invited Talk)

  • Yossi Matias
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3720)


With the proliferation of data intensive applications, it has become necessary to develop new techniques to handle massive data sets. Traditional algorithmic techniques and data structures are not always suitable to handle the amount of data that is required and the fact that the data often streams by and cannot be accessed again. A field of research established over the past decade is that of handling massive data sets using data synopses, and developing algorithmic techniques for data stream models. We will discuss some of the research work that has been done in the field, and provide a decades’ perspective to data synopses and data streams.


  1. 1.
    Alon, N., Matias, Y., Szegedy, M.: The space complexity of approximating the frequency moments. J. of Computer and System Sciences 58, 137–147 (1999); STOC 1996 Special IssuezbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models and issues in data stream systems. In: Proc. Symposium on Principles of Database Systems, pp. 1–16 (2002)Google Scholar
  3. 3.
    Gibbons, P.B., Matias, Y.: Synopses data structures for massive data sets. External memory algorithms, DIMACS Series Discrete Math. & TCS, AMS 50 (1999), Also SODA 1999Google Scholar
  4. 4.
    Matias, Y.: Data streams and data synopses for massive data sets,
  5. 5.
    Muthukrishnan, S.: Data streams: Algorithms and applications,
  6. 6.
    Vitter, J.S.: External memory algorithms and data structures. ACM Comput Surv. 33(2), 209–271 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yossi Matias
    • 1
  1. 1.Tel Aviv University, HyperRoll Inc., Stanford University 

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