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dc.creatorVelentzas P., Moutafis P., Mavrommatis G.en
dc.date.accessioned2023-01-31T10:31:25Z
dc.date.available2023-01-31T10:31:25Z
dc.date.issued2020
dc.identifier10.1145/3437120.3437343
dc.identifier.isbn9781450388979
dc.identifier.urihttp://hdl.handle.net/11615/80560
dc.description.abstractThe k Nearest Neighbor (k-NN) query is a common spatial query that appears in several big data applications. We propose and implement a new GPU-based algorithm for the k-NN query, which improves our previous Symmetric Progression Partitioning method (SPP) by adding a heap buffer. We experimentally prove that the addition of heap speeds up the k-NN query, especially in larger values of k. Using random, synthetic and real datasets, we present an extensive experimental performance comparison against two of our algorithms. This comparison shows that the new algorithm excels in all the conducted experiments. © 2020 ACM.en
dc.language.isoenen
dc.sourceACM International Conference Proceeding Seriesen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85102383886&doi=10.1145%2f3437120.3437343&partnerID=40&md5=39b714a6c03c128b00ccf5ad90efe69d
dc.subjectGraphics processing uniten
dc.subjectMotion compensationen
dc.subjectBig data applicationsen
dc.subjectGPU-based algorithmsen
dc.subjectK nearest neighbor queriesen
dc.subjectK-nearest neighborsen
dc.subjectPartitioning methodsen
dc.subjectPerformance comparisonen
dc.subjectReal data setsen
dc.subjectSpatial queriesen
dc.subjectNearest neighbor searchen
dc.subjectAssociation for Computing Machineryen
dc.titleAn Improved GPU-based Algorithmfor Processing the k Nearest Neighbor Queryen
dc.typeconferenceItemen


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