Supporting Hard Queries over Probabilistic Preferences

Preference analysis is widely applied in various domains such as social choice and e-commerce. A recently proposed framework augments the relational database with a preference relation that represents uncertain preferences in the form of statistical ranking models, and provides methods to evaluate Conjunctive Queries (CQs) that express preferences among item attributes. In this paper, we explore the evaluation of queries that are more general and harder to compute. The main focus of this paper is on a class of CQs that cannot be evaluated by previous work. These queries are provably hard since relate variables that represent items being compared. To overcome this hardness, we instantiate these variables with their domain values, rewrite hard CQs as unions of such instantiated queries, and develop several exact and approximate solvers to evaluate these unions of queries. We demonstrate that exact solvers that target specific common kinds of queries are far more efficient than general solvers. Further, we demonstrate that sophisticated approximate solvers making use of importance sampling can be orders of magnitude more efficient than exact solvers, while showing good accuracy. In addition to supporting provably hard CQs, we also present methods to evaluate an important family of count queries, and of top-k queries.