Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/75818
Title: Reducing uncertainty of probabilistic top-k ranking via pairwise crowdsourcing
Authors: Lin, X
Xu, JL
Hu, HB 
Fan, Z
Keywords: Crowdsourcing
Top-k ranking
Uncertain data management
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on knowledge and data engineering, 2017, v. 29, no. 10, p. 2290-2303 How to cite?
Journal: IEEE transactions on knowledge and data engineering 
Abstract: Probabilistic top-k ranking is an important and well-studied query operator in uncertain databases. However, the quality of top-k results might be heavily affected by the ambiguity and uncertainty of the underlying data. Uncertainty reduction techniques have been proposed to improve the quality of top-k results by cleaning the original data. Unfortunately, most data cleaning models aim to probe the exact values of the objects individually and therefore do not work well for subjective data types, such as user ratings, which are inherently probabilistic. In this paper, we propose a novel pairwise crowdsourcing model to reduce the uncertainty of top-k ranking using a crowd of domain experts. Given a crowdsourcing task of limited budget, we propose efficient algorithms to select the best object pairs for crowdsourcing that will bring in the highest quality improvement. Extensive experiments show that our proposed solutions outperform a random selection method by up to 30 times in terms of quality improvement of probabilistic top-k ranking queries. In terms of efficiency, our proposed solutions can reduce the elapsed time of a brute-force algorithm from several days to one minute.
URI: http://hdl.handle.net/10397/75818
ISSN: 1041-4347
EISSN: 1558-2191
DOI: 10.1109/TKDE.2017.2717830
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