Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/75818
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dc.contributor.authorLin, Xen_US
dc.contributor.authorXu, JLen_US
dc.contributor.authorHu, HBen_US
dc.contributor.authorFan, Zen_US
dc.date.accessioned2018-05-10T02:54:41Z-
dc.date.available2018-05-10T02:54:41Z-
dc.date.issued2017-
dc.identifier.citationIEEE transactions on knowledge and data engineering, 2017, v. 29, no. 10, p. 2290-2303en_US
dc.identifier.issn1041-4347-
dc.identifier.urihttp://hdl.handle.net/10397/75818-
dc.description.abstractProbabilistic 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.en_US
dc.description.sponsorshipDepartment of Electronic and Information Engineeringen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.ispartofIEEE transactions on knowledge and data engineeringen_US
dc.subjectCrowdsourcingen_US
dc.subjectTop-k rankingen_US
dc.subjectUncertain data managementen_US
dc.titleReducing uncertainty of probabilistic top-k ranking via pairwise crowdsourcingen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage2290-
dc.identifier.epage2303-
dc.identifier.volume29-
dc.identifier.issue10-
dc.identifier.doi10.1109/TKDE.2017.2717830-
dc.identifier.isiWOS:000410643300017-
dc.identifier.eissn1558-2191-
dc.identifier.rosgroupid2017004570-
dc.description.ros2017-2018 > Academic research: refereed > Publication in refereed journal-
dc.description.validate201805 bcrc-
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