Efficient and Effective Similarity Search over Probabilistic Data based on Earth Mover's Distance

dc.contributor.authorXU, Jiaen_US
dc.contributor.authorZHANG, Zhenjieen_US
dc.contributor.authorTUNG, Anthony K.H.en_US
dc.contributor.authorYU, Geen_US
dc.date.accessioned2010-05-05T08:36:22Zen_US
dc.date.accessioned2017-01-23T07:00:14Z
dc.date.available2010-05-05T08:36:22Zen_US
dc.date.available2017-01-23T07:00:14Z
dc.date.issued2010-05-03en_US
dc.description.abstractProbabilistic data is coming as a new deluge along with the technical advances on geographical tracking, multimedia processing, sensor network and RFID. While similarity search is an important functionality supporting the manipulation of probabilistic data, it raises new challenges to traditional relational database. The problem stems from the limited e®ectiveness of the distance metric supported by the existing database system. On the other hand, some complicated distance operators have been proved their values for better distinguishing ability in probabilistic domain. In this paper, we discuss the similarity search problem with the Earth Mover's Distance, which is the most successful distance metric on probabilistic histograms and an expensive operator with cubic complexity. We present a new database approach to answer range queries and k-nearest neighbor queries on probabilistic data, on the basis of Earth Mover's Distance. Our solution utilizes the primal-dual theory in linear programming and deploys B+ tree index structures for e®ective candidate pruning. Extensive experiments show that our proposal dramatically improves the scalability of probabilistic databases.en_US
dc.format.extent1357840 bytesen_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.urihttps://dl.comp.nus.edu.sg/xmlui/handle/1900.100/3178en_US
dc.language.isoenen_US
dc.relation.ispartofseriesTRA5/10en_US
dc.titleEfficient and Effective Similarity Search over Probabilistic Data based on Earth Mover's Distanceen_US
dc.typeTechnical Reporten_US
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