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Efficient k-Nearest Neighbor Searches for Parallel Multidimensional Index Structures

机译:高效的K-最近邻权对并行多维索引结构的搜索

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In this paper, we propose a parallel multidimensional index structure and range search and k-NN search methods for the index structures. The proposed index structure is nP(processor)-n×mD(disk) architecture which is the hybrid type of nP-nD and 1P-nD. Its node structure increases fan-out and reduces the height of an index tree. Also, the proposed range search methods are designed to maximize I/O parallelism of the index structure. Finally, we present a new method to transform k-NN queries to range search queries. Through various experiments, it is shown that the proposed method outperforms other parallel index structures.
机译:在本文中,我们提出了一个平行的多维索引结构和范围搜索和k-nn搜索方法的索引结构。该索引结构是NP(处理器)-N×MD(盘)架构,其是NP-ND和1P-ND的混合类型。其节点结构增加了扇出并减少了索引树的高度。此外,所提出的范围搜索方法旨在最大化索引结构的I / O平行性。最后,我们提出了一种将k-nn查询转换为范围搜索查询的新方法。通过各种实验,示出了所提出的方法优于其他平行索引结构。

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