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Top-k Queries over Uncertain Scores

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dc.contributor.author LIU, Qing en_US
dc.contributor.author BASU, Debabrota en_US
dc.contributor.author ABDESSALEM, Talel en_US
dc.contributor.author BRESSAN, Stephane en_US
dc.date.accessioned 2016-09-29T02:12:55Z en_US
dc.date.accessioned 2017-01-23T06:59:42Z
dc.date.available 2016-09-29T02:12:55Z en_US
dc.date.available 2017-01-23T06:59:42Z
dc.date.issued 2016-09-03 en_US
dc.identifier.uri http://hdl.handle.net/1900.100/6005 en_US
dc.description.abstract Modern recommendation systems leverage some forms of collaborative user or crowd sourced collection of information. For instance, services like TripAdvisor, Airbnb and HungyGoWhere rely on user-generated content to describe and classify hotels, vacation rentals and restaurants. By nature of such independent collection of information, the multiplicity, diversity and varying quality of the information collected result in uncertainty. Objects, such as the services o ffered by hotels, vacation rentals and restaurants, have uncertain scores for their various features. In this context, ranking of uncertain data becomes a crucial issue. Several data models for uncertain data and several semantics for probabilistic top-k queries have been proposed in the literature. We consider here a model of objects with uncertain scores given as probability distributions and the semantics proposed by the state of the art reference work of Soliman, Hyas and Ben-David. In this paper, we explore the design space of Metropolis-Hastings Markov chain Monte Carlo algorithms for answering probabilistic top-k queries over a database of objects with uncertain scores. We are able to devise several algorithms that yield better performance than the reference algorithm. We empirically and comparatively prove the eff ectiveness and effi ciency of these new algorithms. en_US
dc.language.iso en en_US
dc.relation.ispartofseries ;TRA9/16 en_US
dc.title Top-k Queries over Uncertain Scores en_US
dc.type Technical Report en_US


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