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dc.contributor.advisorKörpeoğlu, İbrahim
dc.contributor.authorDilek, Merve
dc.date.accessioned2016-01-08T18:15:29Z
dc.date.available2016-01-08T18:15:29Z
dc.date.issued2011
dc.identifier.urihttp://hdl.handle.net/11693/15244
dc.descriptionAnkara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2011.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2011.en_US
dc.descriptionIncludes bibliographical references leaves 56-59.en_US
dc.description.abstractSimilarity searching is the task of retrieval of relevant information from datasets. We are particularly interested in datasets that contain complex and unstructured data such as images, videos, audio recordings, protein and DNA sequences. The relevant information is typically defined using one of two common query types: a range query involves retrieval of all the objects within a specified distance to the query object; whereas a k-nearest neighbor query deals with obtaining k closest database objects to the query object. A variety of index structures based on the notion of metric spaces have been offered to process these two query types. The query performances of the proposed index structures have not been satisfactory particularly for high dimensional datasets. As a solution, various approximate similarity search methods offering the users a quality/time trade-off have been proposed. The rationale is that the users might be willing to tolerate query precision to retrieve query results relatively faster. The proposed approximate searching schemes usually have strong connections to the underlying data structures, making the comparison of the quality of the essence of their ideas difficult. In this thesis we investigate various approximation approaches to decrease the response time of similarity queries. These approaches use a variety of statistics about the dataset in order to obtain dynamic (at the time of querying) and specific guidance on the approximation for each query object individually. The experiments are performed on top of a simple underlying pivot-based index structure to minimize the effects of the index to our approximation schemes. The results show that it is possible to improve the performance/precision of the approximation based on data and query object sensitive guidance.en_US
dc.description.statementofresponsibilityDilek, Merveen_US
dc.format.extentxiii, 59 leavesen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectApproximate Similarity Searchingen_US
dc.subjectMetric Spacesen_US
dc.subjectRange Queryen_US
dc.subject.lccQA611.28 .D55 2011en_US
dc.subject.lcshMetric spaces.en_US
dc.subject.lcshData mining.en_US
dc.subject.lcshDatabase searching.en_US
dc.titleData sensitive approximate query approaches in metric spacesen_US
dc.typeThesisen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US


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