Query Evaluation on Probabilistic Databases

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