dc.creator | Velentzas P., Moutafis P., Mavrommatis G. | en |
dc.date.accessioned | 2023-01-31T10:31:25Z | |
dc.date.available | 2023-01-31T10:31:25Z | |
dc.date.issued | 2020 | |
dc.identifier | 10.1145/3437120.3437343 | |
dc.identifier.isbn | 9781450388979 | |
dc.identifier.uri | http://hdl.handle.net/11615/80560 | |
dc.description.abstract | The 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.iso | en | en |
dc.source | ACM International Conference Proceeding Series | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102383886&doi=10.1145%2f3437120.3437343&partnerID=40&md5=39b714a6c03c128b00ccf5ad90efe69d | |
dc.subject | Graphics processing unit | en |
dc.subject | Motion compensation | en |
dc.subject | Big data applications | en |
dc.subject | GPU-based algorithms | en |
dc.subject | K nearest neighbor queries | en |
dc.subject | K-nearest neighbors | en |
dc.subject | Partitioning methods | en |
dc.subject | Performance comparison | en |
dc.subject | Real data sets | en |
dc.subject | Spatial queries | en |
dc.subject | Nearest neighbor search | en |
dc.subject | Association for Computing Machinery | en |
dc.title | An Improved GPU-based Algorithmfor Processing the k Nearest Neighbor Query | en |
dc.type | conferenceItem | en |