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Encyclopedia of Database Systems pp 80–83Cite as

AMS Sketch

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Synonyms

AGMS sketch; Sketch; Tug-of-war sketch

Definition

AMS sketches are randomized summaries of the data that can be used to compute aggregates such as the second frequency moment (the self-join size) and sizes of joins. AMS sketches can be viewed as random projections of the data in the frequency domain on ± 1 pseudo-random vectors. The key property of AMS sketches is that the product of projections on the same random vector of frequencies of the join attribute of two relations is an unbiased estimate of the size of join of the relations. While a single AMS sketch is inaccurate, multiple such sketches can be computed and combined using averages and medians to obtain an estimate of any desired precision.

Historical Background

The AMS sketches were introduced in 1996 by Noga Alon, Yossi Matias, and Mario Szegedy as part of a suit of randomized algorithms for approximate computation of frequency moments. The same authors, together with Phillip Gibbons, extended the second frequency...

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Recommended Reading

  1. Alon N., Gibbons P.B., Matias Y., and Szegedy M. Tracking join and self-join sizes in limited storage. J. Comput. Syst. Sci., 64(3):719–747, 2002.

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  2. Alon N., Matias Y., and Szegedy M. The space complexity of approximating the frequency moments. In Proc. 28th Annual ACM Symp. on Theory of Computing, 1996, pp. 20–29.

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  3. Charikar M., Chen K., and Farach-Colton M. Finding frequent items in data streams. In Proc. 29th Int. Colloquium on Automata, Languages and Programming, 2002, pp. 693–703.

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  4. Cormode G. and Garofalakis M. Sketching streams through the net: distributed approximate query tracking. In Proc. 31st Int. Conf. on Very Large Data Bases, 2005, pp. 13–24.

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  5. Das A., Gehrke J., and Riedewald M. Approximation techniques for spatial data. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 2004, pp. 695–706.

    Google Scholar 

  6. Dobra A., Garofalakis M., Gehrke J., and Rastogi R. Processing complex aggregate queries over data streams. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 2002, pp. 61–72.

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  7. Rusu F. and Dobra A. 2007.Pseudo-random number generation for sketch-based estimations. ACM Trans. Database Syst., 32(2):11,

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  8. Rusu F. and Dobra A. Statistical Analysis of Sketch Estimators. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 2007, pp. 187–198.

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Author information

Authors and Affiliations

  1. University of Florida, Gainesville FL, USA

    Alin Dobra

Authors
  1. Alin Dobra
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Editor information

Editors and Affiliations

  1. College of Computing, Georgia Institute of Technology, 266 Ferst Drive, 30332-0765, Atlanta, GA, USA

    LING LIU (Professor) (Professor)

  2. Database Research Group David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada

    M. TAMER ÖZSU (Professor and Director, University Research Chair) (Professor and Director, University Research Chair)

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© 2009 Springer Science+Business Media, LLC

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Dobra, A. (2009). AMS Sketch. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_16

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