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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Efficient Parallel Processing for K-Nearest-Neighbor Search in Spatial Databases
verfasst von : Yunjun Gao, Ling Chen, Gencai Chen, Chun Chen
Erschienen in: Computational Science and Its Applications - ICCSA 2006
Verlag: Springer Berlin Heidelberg
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Even though the problem of
k
nearest neighbor (
k
NN) query is well-studied in serial environment, there is little prior work on parallel
k
NN search processing in parallel one. In this paper, we present the first Best-First based Parallel
k
NN (BFP
k
NN) query algorithm in a multi-disk setting, for efficient handling of
k
NN retrieval with arbitrary values of
k
by parallelization. The core of our method is to access more entries from multiple disks simultaneously and enable several effective pruning heuristics to discard non-qualifying entries. Extensive experiments with real and synthetic datasets confirm that BFP
k
NN significantly outperforms its competitors in both efficiency and scalability.