Dissociation and Propagation for Efficient Query Evaluation over Probabilistic Databases

Queries over probabilistic databases are either safe, in which case they can be evaluated entirely in a relational database engine, or unsafe, in which case they need to be evaluated with a general-purpose inference engine at a high cost. We propose a new approach by which every query is evaluated inside the database engine, by using a new method called dissociation. A dissociated query is obtained by adding extraneous variables to some atoms until the query becomes safe. We show that the probability of the original query and that of the dissociated query correspond to two well-known scoring functions on graphs, namely graph reliability (which is #P-hard), and the propagation score (which is related to PageRank and is in PTIME): When restricted to graphs, standard query probability is graph reliability, while the dissociated probability is the propagation score. We define a propagation score for self-join-free conjunctive queries and prove that it is always an upper bound for query reliability, and that both scores coincide for all safe queries. Given the widespread and successful use of graph propagation methods in practice, we argue for the dissociation method as a highly efficient way to rank probabilistic query results, especially for those queries which are highly intractable for exact probabilistic inference.