Query-Based Why-Not Provenance with NedExplain

Nicole Bidoit 1, 2 Melanie Herschel 2, 1 Katerina Tzompanaki 1, 2
2 OAK - Database optimizations and architectures for complex large data
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : With the increasing amount of available data and transformations manipulating the data, it has become essential to analyze and debug data transformations. A sub-problem of data transformation analysis is to understand why some data are not part of the result of a relational query. One possibility to explain the lack of data in a query result is to identify where in the query we lost data pertinent to the expected outcome. A first approach to this so called why-not provenance has been recently proposed, but we show that this first approach has some shortcomings. To overcome these shortcomings, we propose \ned, an algorithm to explain data missing from a query result. NedExplain computes the why-not provenance for monotone relational queries with aggregation. After providing necessary definitions, this paper contributes a detailed description of the algorithm. A comparative evaluation shows that it is both more efficient and effective than the state-of-the-art approach.
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Nicole Bidoit, Melanie Herschel, Katerina Tzompanaki. Query-Based Why-Not Provenance with NedExplain. Extending Database Technology (EDBT), Mar 2014, Athens, Greece. ⟨hal-00962157⟩

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