Approximate Lifted Inference with Probabilistic Databases

TL;DR

This paper introduces a novel approximate inference method for probabilistic databases that evaluates multiple query plans to efficiently estimate query probabilities, leveraging schema information and optimization techniques.

Contribution

It presents a new algorithm for approximate query evaluation that generalizes existing PTIME results and efficiently computes bounds using minimal necessary plans.

Findings

The approach effectively estimates query probabilities with tight bounds.

The algorithm generalizes all known PTIME safe query results.

Experimental results demonstrate fast evaluation and improved ranking of query answers.

Abstract

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.