HD-Tree: An Efficient High-Dimensional Virtual Index Structure Using a Half Decomposition Strategy

HD-Tree: An Efficient High-Dimensional Virtual Index Structure Using a Half Decomposition Strategy
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HD-Tree:一种采用半分解策略的高效高维虚拟索引结构

DOI:
10.3390/a13120338
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发表时间:
2020-12
期刊:
影响因子:
2.3
通讯作者:
Zhenwen He
Zhenwen He
中科院分区:
--
文献类型:
--
作者:
Ting Huang;Zhengping Weng;Gang Liu;Zhenwen He

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为了更有效地管理多维点数据,本文提出了一种改进的索引方法,称为HD-树,称为D-树。这两种结构都结合了类似四叉树的分区(使用整数移位操作而不存储内部节点,而只存储叶子)和哈希表(用于搜索存储的节点)。然而,HD-tree采用了一种全新的分解策略,称为半分解策略。这种改进避免了生成只包含少量数据的节点和顺序查找哈希表,从而可以节省存储空间,同时在构建树和查询数据时具有更快的I/O和更好的时间性能。实验结果表明,无论是均匀数据还是非均匀数据,HD-树的时间和空间性能都优于D-树,而D-树受数据分布的影响较小。
To manage multidimensional point data more efficiently, this paper presents an improvement, called HD-tree, of a previous indexing method, called D-tree. Both structures combine quadtree-like partitioning (using integer shift operations without storing internal nodes, but only leaves) and hash tables (for searching for the nodes stored). However, the HD-tree follows a brand-new decomposition strategy, which is called half decomposition strategy. This improvement avoids the generation of nodes containing only a small amount of data and the sequential search of the hash table, so that it can save storage space while having faster I/O and better time performance when building the tree and querying data. The results demonstrate convincingly that the time and space performance of HD-tree is better than that of D-tree regardless of uniform or uneven data, which are less affected by data distribution.
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