Query size estimation by adaptive sampling (extended abstract)

We present an adaptive, random sampling algorithm for estimating the size of general queries. The algorithm can be used for any query Q over a database D such that 1) for some n, the answer to Q can be partitioned into n disjoint subsets Q1, Q2, …, Qn, and 2) for 1 ≤ i ≤ n, the size of Qi is bounded by some function b(D, Q), and 3) there is some algorithm by which we can compute the size of Qi, where i is chosen randomly. We consider the performance of the algorithm on three special cases of the algorithm: join queries, transitive closure queries, and general recursive Datalog queries.