Approximate Lifted Inference with Probabilistic Databases
This work addresses efficient query processing in probabilistic databases, which is incremental as it builds on existing safe query methods with optimizations.
The paper tackles the problem of approximate evaluation of #P-hard queries in probabilistic databases by evaluating a fixed number of query plans to compute upper bounds and taking their minimum, with an algorithm that generalizes known PTIME results and optimizes plan evaluation. Experimental results demonstrate fast performance and provide insights into probabilistic methods for ranking query answers.
This paper proposes a new approach for approximate evaluation of #P-hard queries with probabilistic databases. In our approach, every query is evaluated entirely in the database engine by evaluating a fixed number of query plans, each providing an upper bound on the true probability, then taking their minimum. We provide an algorithm that takes into account important schema information to enumerate only the minimal necessary plans among all possible plans. Importantly, this algorithm is a strict generalization of all known results of PTIME self-join-free conjunctive queries: A query is safe if and only if our algorithm returns one single plan. We also apply three relational query optimization techniques to evaluate all minimal safe plans very fast. We give a detailed experimental evaluation of our approach and, in the process, provide a new way of thinking about the value of probabilistic methods over non-probabilistic methods for ranking query answers.