Efficiently and Effectively Answering Why-Not Questions based on Provenance PolynomialsReport as inadecuate




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1 LaHDAK - LRI - Données et Connaissances Massives et Hétérogènes LRI LRI - Laboratoire de Recherche en Informatique 2 LRI - Laboratoire de Recherche en Informatique 3 UP11 - Université Paris-Sud - Paris 11 4 OAK - Database optimizations and architectures for complex large data LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623

Abstract : The problem of answering Why-Not questions consists in explaining why the result of a querydoes not contain some expected data, i.e., missing answers. To solve this problem, we resort to identifyingwhere in the query, data relevant to the missing answer were lost. Existing algorithms producing suchquery-based explanations rely on a query tree representation, potentially leading to different or partialexplanations. This significantly impairs on the effectiveness of computed explanations. Here we present aneffective, query-tree independent representation of query-based explanations, for a wide class of Why-Notquestions, based on provenance polynomials. We further describe an algorithm that efficiently computes thecomplete set of these explanations. An experimental evaluation validates our statements

Keywords : Why-Not questions data provenance





Author: Nicole Bidoit - Melanie Herschel - Katerina Tzompanaki -

Source: https://hal.archives-ouvertes.fr/



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