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dc.contributor.authorÇelik, Cengizen_US
dc.coverage.spatialBelfast, UK
dc.date.accessioned2016-02-08T11:38:42Z
dc.date.available2016-02-08T11:38:42Z
dc.date.issued2008-04en_US
dc.identifier.urihttp://hdl.handle.net/11693/26888
dc.descriptionDate of Conference: 11-12 April 2008
dc.descriptionConference name: First International Workshop on Similarity Search and Applications, SISAP 2008
dc.description.abstractAmong the metric space indexing methods, AESA is known to produce the lowest query costs in terms of the number of distance computations. However, its quadratic construction cost and space consumption makes it infeasiblefor large dataseis. There have been some work on reducing the space requirements of AESA. Instead of keeping all the distances between objects, LAESA appoints a subset of the database as pivots, keeping only the distances between objects and pivots. Kvp uses the idea of prioritizing the pivots based on their distances to objects, only keeping pivot distances that it evaluates as promising. FQA discretizes the distances using a fixed amount of bits per distance instead of using system's floating point types. Varying the number of bits to produce a performance-space trade-off was also studied in Kvp. Recently, BAESA has been proposed based on the same idea, but using different distance ranges for each pivot. The t-spanner based indexing structure compacts the distance matrix by introducing an approximation factor that makes the pivots less effective. In this work, we show that the Kvp prioritization is oriented toward symmetric distance distributions. We offer a new method that evaluates the effectiveness of pivots in a better fashion by making use of the overall distance distribution. We also simulate the performance of our method combined with distance discretization. Our results show that our approach is able to offer very good space-performance trade-offs compared to AESA and tree-based methods. © 2008 IEEE.en_US
dc.language.isoEnglishen_US
dc.source.titleProceedings - First International Workshop on Similarity Search and Applications, SISAP 2008en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/SISAP.2008.22en_US
dc.subjectDistance computationsen_US
dc.subjectDistance matrixen_US
dc.subjectSymmetric distance distributionsen_US
dc.subjectCostsen_US
dc.subjectData structuresen_US
dc.subjectDatabase systemsen_US
dc.subjectMatrix algebraen_US
dc.subjectQuery processingen_US
dc.subjectIndexing (of information)en_US
dc.titleEffective use of space for pivot-based metric indexing structuresen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Computer Engineering
dc.citation.spage113en_US
dc.citation.epage120en_US
dc.identifier.doi10.1109/SISAP.2008.22en_US
dc.publisherIEEE


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