Top-k querying of unknown values under order constraints

Antoine Amarilli, Yael Amsterdamer, Tova Milo, Pierre Senellart

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication20th International Conference on Database Theory, ICDT 2017
EditorsGiorgio Orsi, Michael Benedikt
PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
ISBN (Electronic)9783959770248
DOIs
StatePublished - 1 Mar 2017
Event20th International Conference on Database Theory, ICDT 2017 - Venice, Italy
Duration: 21 Mar 201724 Mar 2017

Publication series

NameLeibniz International Proceedings in Informatics, LIPIcs
Volume68
ISSN (Print)1868-8969

Conference

Conference20th International Conference on Database Theory, ICDT 2017
Country/TerritoryItaly
CityVenice
Period21/03/1724/03/17

Bibliographical note

Publisher Copyright:
© Antoine Amarilli, Yael Amsterdamer, Tova Milo, and Pierre Senellart; licensed under Creative Commons License CC-BY 20th International Conference on Database Theory (ICDT 2017).

Keywords

  • Crowdsourcing
  • Interpolation
  • Partial order
  • Uncertainty
  • Unknown values

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