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