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oai:drops-oai.dagstuhl.de:7045 2024-03-06T10:39:24Z ddc:004 open_access

Top-k Querying of Unknown Values under Order Constraints Amarilli, Antoine Amsterdamer, Yael Milo, Tova Senellart, Pierre uncertainty partial order unknown values crowdsourcing interpolation 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Antoine Amarilli and Yael Amsterdamer and Tova Milo and Pierre Senellart 2017 Is Part Of LIPIcs, Volume 68, 20th International Conference on Database Theory (ICDT 2017) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.ICDT.2017.5 urn:nbn:de:0030-drops-70457 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICDT.2017.5 eng https://creativecommons.org/licenses/by/3.0/legalcode