Efficient Evaluation of HAVING Queries on a Probabilistic Database

We study the evaluation of positive conjunctive queries with Boolean aggregate tests (similar to HAVING queries in SQL) on probabilistic databases. Our motivation is to handle aggregate queries over imprecise data resulting from information integration or information extraction. More precisely, we study conjunctive queries with predicate aggregates using MIN, MAX, COUNT, SUM, AVG or COUNT(DISTINCT) on probabilistic databases. Computing the precise output probabilities for positive conjunctive queries (without HAVING) is ]P-hard, but is in P for a restricted class of queries called safe queries. Further, for queries without self-joins either a query is safe or its data complexity is ]P-Hard, which shows that safe queries exactly capture tractable queries without self-joins. In this paper, for each aggregate above, we find a class of queries that exactly capture efficient evaluation for HAVING queries without self-joins. Our algorithms use a novel technique to compute the marginal distributions of elements in a semiring, which may be of independent interest.