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Adaptive Sampling

Adaptive Sampling. Based on a hot-list algorithm by Gibbons and Matias (SIGMOD 1998) Sample elements from the input set Frequently occurring elements will be sampled more often Sampling probability determined at runtime, according to the allowed memory usage

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Adaptive Sampling

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  1. Adaptive Sampling • Based on a hot-list algorithm by Gibbons and Matias (SIGMOD 1998) • Sample elements from the input set • Frequently occurring elements will be sampled more often • Sampling probability determined at runtime, according to the allowed memory usage • Tradeoff between overhead and accuracy • Give an estimate of the sample’s accuracy

  2. Concise Samples • Uniform random sampling • Maintain an <id, count> pair for each element • The sample size can be much larger than the memory size • For skewed input sets the gain is much larger • Sampling is not applied at every block • Vitter’s reservoir sampling

  3. Concise Samples

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