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Sampling Techniques for Statistical Databases

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Encyclopedia of Database Systems

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Definition

A sampling technique is a method by which one inspects only a small portion of data from a database to reduce the time to compute an aggregate query, but simultaneously ensuring that result computed on the sample faithfully represents the true results of the query for the entire data population.

Example

Acceptance-Rejection sampling(AR sampling) is sampling technique.

Key Points

Sampling is used in a database for different reasons such as (i) to estimate the results of aggregate queries (e.g., SUM, COUNT, orAVERAGE), (ii) to retrieve a sample of records from a database query for subsequent processing, (iii) for internal use by the query optimizer for selectivity estimation, (iv)to provide privacy protection for records on individuals contained in statistical databases. It has been determined that fixed size random sampling of data does not yield a true representation of the population. Acceptance/rejection (A/R) samplingis used to construct weighted samples in which the...

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Recommended Reading

  1. Olken F, Rotem D. Random sampling from databases: a survey. Stat Comput. 1995;5:25–42.

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Authors and Affiliations

  1. San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive, 92093-0505, La Jolla, CA, USA

    Amarnath Gupta

Authors
  1. Amarnath Gupta
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Corresponding author

Correspondence to Amarnath Gupta .

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. San Diego Supercomputer Center, Univ. of California San Diego, 9500 Gilman Drive, 92093-0505, La Jolla, CA, USA

    Amarnath Gupta

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Gupta, A. (2016). Sampling Techniques for Statistical Databases. In: Liu, L., Özsu, M. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-7993-3_1293-2

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  • DOI: https://doi.org/10.1007/978-1-4899-7993-3_1293-2

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  • Publisher Name: Springer, New York, NY

  • Online ISBN: 978-1-4899-7993-3

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

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