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Title: Exact Processing of Uncertain Top-k Queries in Multi-criteria Settings

Citation Type: Miscellaneous

Publication Year: 2018

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Abstract: Traditional rank-aware processing assumes a dataset that contains available options to cover a specific need (e.g., restaurants, hotels, etc) and users who browse that dataset via top-k queries with linear scoring functions, i.e., by rank- ing the options according to the weighted sum of their at- tributes, for a set of given weights. In practice, however, user preferences (weights) may only be estimated with bounded accuracy, or may be inherently uncertain due to the inability of a human user to specify exact weight values with abso- lute accuracy. Motivated by this, we introduce the uncertain top-k query (U T K ). Given uncertain preferences, that is, an approximate description of the weight values, the UTK query reports all options that may belong to the top-k set. A second version of the problem additionally reports the ex- act top-k set for each of the possible weight settings. We develop a scalable processing framework for both UTK ver- sions, and demonstrate its efficiency using standard bench- mark datasets.

Url: http://www.vldb.org/pvldb/vol11/p866-mouratidis.pdf

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Authors: Mouratidis, Kyriakos; Tang, Bo

Publisher: Singapore Management University

Data Collections: IPUMS USA

Topics: Other, Population Data Science

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