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Approximate Query Answering over Incomplete Data

  • Nicola Fiorentino
  • Cristian Molinaro
  • Irina TrubitsynaEmail author
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 880)

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.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Nicola Fiorentino
    • 1
  • Cristian Molinaro
    • 1
  • Irina Trubitsyna
    • 1
    Email author
  1. 1.DIMES, University of CalabriaRendeItaly

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