Efficient query evaluation on probabilistic databases

It is shown that the data complexity of some queries is #P-complete, which implies that these queries do not admit any efficient evaluation methods, and an optimization algorithm is described that can compute efficiently most queries.

Abstract

We describe a framework for supporting arbitrarily complex SQL queries with "uncertain" predicates. The query semantics is based on a probabilistic model and the results are ranked, much like in Information Retrieval. Our main focus is query evaluation. We describe an optimization algorithm that can compute efficiently most queries. We show, however, that the data complexity of some queries is #P-complete, which implies that these queries do not admit any efficient evaluation methods. For these queries we describe both an approximation algorithm and a Monte-Carlo simulation algorithm.