Top-k Querying of Unknown Values under Order Constraints (Extended Version)

Abstract : Many practical scenarios make it necessary to evaluate top-k queries over data items with partially unknown values. This paper considers a setting where the values are taken from a numerical domain, and where some partial order constraints are given over known and unknown values: under these constraints, we assume that all possible worlds are equally likely. Our work is the first to propose a principled scheme to derive the value distributions and expected values of unknown items in this setting, with the goal of computing estimated top-k results by interpolating the unknown values from the known ones. We study the complexity of this general task, and show tight complexity bounds, proving that the problem is intractable, but can be tractably approximated. We then consider the case of tree-shaped partial orders, where we show a constructive PTIME solution. We also compare our problem setting to other top-k definitions on uncertain data.
Type de document :
Pré-publication, Document de travail
32 pages, 1 figure, 1 algorithm, 51 references. Extended version of paper at ICDT'17. 2017
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https://hal.inria.fr/hal-01439310
Contributeur : Pierre Senellart <>
Soumis le : jeudi 19 janvier 2017 - 19:15:52
Dernière modification le : mardi 19 juin 2018 - 10:44:02

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  • HAL Id : hal-01439310, version 1
  • ARXIV : 1701.02634

Citation

Antoine Amarilli, Yael Amsterdamer, Tova Milo, Pierre Senellart. Top-k Querying of Unknown Values under Order Constraints (Extended Version). 32 pages, 1 figure, 1 algorithm, 51 references. Extended version of paper at ICDT'17. 2017. 〈hal-01439310〉

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