Approximate Query Answering over Incomplete Data

Abstract

In era of Big Data different applications face the problem of dealing with incomplete data. In the presence of incomplete databases, certain answers are a principled semantics of query answering. Unfortunately, the computation of certain query answers is a coNP-hard problem. To make query answering feasible in practice, recent research has focused on developing polynomial time algorithms computing a sound (but possibly incomplete) set of certain answers. In this chapter, we discuss several recently proposed approximation algorithms, along with a system prototype implementing them and experimental evaluation. The central tools are conditional tables and the conditional evaluation of relation algebra. Different evaluation strategies can be applied, with more accurate ones having higher complexity, but returning more certain answers, thereby enabling users to choose the technique that best meets their needs in terms of balance between efficiency and quality of the results.