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Encyclopedia of Database Systems pp 2299–2306Cite as

Monte Carlo Methods for Uncertain Data

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Synonyms

Sampling methods; Simulation methods; Stochastic methods

Definition

Uncertain datasets do not contain specific data values but rather representations of probability distributions over “possible worlds,” that is, over possible realizations of the dataset. Queries over such datasets result in a probability distribution over possible answers, where each possible answer corresponds to the query result in one of the possible worlds. In this setting, the goal of query processing is to compute the query-result distribution or perhaps features of this distribution such as marginal probabilities, moments, modes, and quantiles. Basic Monte Carlo methods approximate the query-result distribution by, in essence, repeatedly generating possible-world instances and computing the query answer on each instance. The resulting samples from the query-result distribution are used to estimate quantities of interest using statistical methods. More sophisticated techniques try to improve on the...

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Author information

Authors and Affiliations

  1. IBM Almaden Research Center, San Jose, CA, USA

    Peter J. Haas

Authors
  1. Peter J. Haas
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Corresponding author

Correspondence to Peter J. Haas .

Editor information

Editors and Affiliations

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

    Ling Liu

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

    M. Tamer Özsu

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Haas, P.J. (2018). Monte Carlo Methods for Uncertain Data. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_80692

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