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 <italic>Q</italic> over a database <italic>D</italic> such that 1) for some <italic>n</italic>, the answer to <italic>Q</italic> can be partitioned into <italic>n</italic> disjoint subsets <italic>Q</italic><subscrpt>1</subscrpt>, <italic>Q</italic><subscrpt>2</subscrpt>, …, <italic>Q<subscrpt>n</subscrpt></italic>, and 2) for 1 ≤ <italic>i</italic> ≤ <italic>n</italic>, the size of <italic>Q<subscrpt>i</subscrpt></italic> is bounded by some function <italic>b</italic>(<italic>D, Q</italic>), and 3) there is some algorithm by which we can compute the size of <italic>Q<subscrpt>i</subscrpt></italic>, where <italic>i</italic> 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.