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 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