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Anytime Top-k Queries On Exact And Fuzzy Data

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Anytime Top-k Queries On Exact And Fuzzy Data

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dc.contributor.author Chaudhari, Bhushan P en_US
dc.date.accessioned 2007-08-23T01:56:29Z
dc.date.available 2007-08-23T01:56:29Z
dc.date.issued 2007-08-23T01:56:29Z
dc.date.submitted May 2006 en_US
dc.identifier.other DISS-1268 en_US
dc.identifier.uri http://hdl.handle.net/10106/322
dc.description.abstract Top-k queries on large multi-attribute data sets are fundamental operations in information retrieval and ranking applications. In this thesis, we initiate research on the anytime behavior of top-k algorithms on exact and fuzzy data. In particular given specific topk algorithms we are interested in studying their progress towards identification of the correct result at any point of the algorithms' execution. We adopt a probabilistic approach where we seek to report at any point the scores of the top-k results the algorithm has identified, as well as associate a confidence with this prediction. Such functionality can be a valuable asset when one is interested to reduce the runtime cost of top-k computations. We show analytically that such probability and confidence are monotone in expectation. We present a thorough experimental evaluation to validate our techniques using both synthetic and real data sets. en_US
dc.description.sponsorship Das, Gautam en_US
dc.language.iso EN en_US
dc.publisher Computer Science & Engineering en_US
dc.title Anytime Top-k Queries On Exact And Fuzzy Data en_US
dc.type M.S. en_US
dc.contributor.committeeChair Das, Gautam en_US
dc.degree.department Computer Science & Engineering en_US
dc.degree.discipline Computer Science & Engineering en_US
dc.degree.grantor University of Texas at Arlington en_US
dc.degree.level masters en_US
dc.degree.name M.S. en_US
dc.identifier.externalLink https://www.uta.edu/ra/real/editprofile.php?onlyview=1&pid=178
dc.identifier.externalLinkDescription Link to Research Profiles

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