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Data Sampling

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Encyclopedia of Database Systems

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Definition

Repeatedly choosing random numbers according to a given distribution is generally referred to as sampling. It is a popular technique for data reduction and approximate query processing. It allows a large set of data to be summarized as a much smaller data set, the sampling synopsis, which usually provides an estimate of the original data with provable error guarantees. One advantage of the sampling synopsis is easy and efficient. The cost of constructing such a synopsis is only proportional to the synopsis size, which makes the sampling complexity potentially sublinear to the size of the original data. The other advantage is that the sampling synopsis represents parts of the original data. Thus, many query processing and data manipulation techniques that are applicable to the original data can be directly applied on the synopsis.

Historical Background

The notion of representing large data sets through small samples dates back to the end of nineteenth century and has led to...

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Recommended Reading

  1. Aggarwal CC. On biased reservoir sampling in the presence of stream evolution. In: Proceedings of 32nd International Conference on Very Large Data Bases; 2006.

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  2. Chaudhuri S, et al. Overcoming limitations of sampling for aggregation queries. In: Proceedings of 17th International Conference on Data Engineering; 2001.

    Google Scholar 

  3. Ganti V, Lee M-L, Ramakrishnan R. ICICLES: self-tuning samples for approximate query answering. In: Proceedings of 28th International Conference on Very Large Data Bases; 2000.

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  4. Gibbons PB, Matias Y. New sampling-based summary statistics for improving approximate query answers. In: Proceedings of ACM SIGMOD International Conference on Management of Data; 1998.

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  5. Kish L. Survey sampling. New York: Wiley; 1965. p. 643. xvi.

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  6. Speegle GD, Donahoo MJ. Using statistical sampling for query optimization in heterogeneous library information systems. In: Proceedings of 20th ACM Annual Conference on Computer Science; 1993.

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  7. Vitter JS. Random sampling with a reservoir. ACM Trans Math Softw. 1985;11(1):37–57.

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Author information

Authors and Affiliations

  1. The Australian e-health Research Center, Brisbane, QLD, Australia

    Qing Zhang

Authors
  1. Qing Zhang
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Correspondence to Qing Zhang .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, Georgia, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, Ontario, Canada

    M. Tamer Özsu

Section Editor information

  1. School of Information Technology and Electrical Engineering, University of Queensland, St Lucia Campus, 4072, Brisbane, QLD, Australia

    Xiaofang Zhou

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Zhang, Q. (2017). Data Sampling. In: Liu, L., Özsu, M. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-7993-3_535-2

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  • DOI: https://doi.org/10.1007/978-1-4899-7993-3_535-2

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4899-7993-3

  • Online ISBN: 978-1-4899-7993-3

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