Approximation algorithms for querying incomplete databases

Certain answers are a widely accepted semantics of query answering over incomplete databases. As their computation is a coNP-hard problem, recent research has focused on developing (polynomial time) evaluation algorithms with correctness guarantees, that is, techniques computing a sound but possibly incomplete set of certain answers. The aim is to make the computation of certain answers feasible in practice, settling for under-approximations. In this paper, we present novel evaluation algorithms with correctness guarantees, which provide better approximations than current techniques, while retaining polynomial time data complexity. The central tools of our approach are conditional tables and the conditional evaluation of queries. We propose different strategies to evaluate conditions, leading to different approximation algorithms—more accurate evaluation strategies have higher running times, but they pay off with more certain answers being returned. Thus, our approach offers a suite of approximation algorithms enabling users to choose the technique that best meets their needs in terms of balance between efficiency and quality of the results.