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Compact N-tree: an indexing structure for distance range queries

机译:Compact N-Tree:距离范围查询的索引结构

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Mobile query processing is, currently, a very active research field. Range and nearest neighbor queries are commonly used in spatiotemporal databases and location based services (LBS). In this paper, we focus on finding nearest neighbors of a query point within a certain distance range. We propose a new indexing structure CN-tree, Compact N-tree, based on a recent indexing technique called N-tree. CN-tree joins efficiency of N-tree's data partitioning scheme to pertinent objects' approximation with minimal bounding rectangles of R-trees which are reported to be the best performing for range search. We show how we use the approximation in constructing CN-tree and, then, how this index can support range queries efficiently by minimizing computation of distances and avoiding overlapping of minimal bounding rectangles. The experimental results through the comparison with the well know R*-tree, show that the proposed CN-tree widely outperforms R*-tree as an in-memory index and it presents competitive performances when used as an in-disk index.
机译:移动查询处理目前是一个非常活跃的研究领域。范围和最近的邻权常用于Spatiotemporal数据库和基于位置的服务(LBS)。在本文中,我们专注于在一定距离范围内找到查询点的最近邻居。我们提出了一种基于最近名为N树的索引技术的新索引结构CN树,紧凑的N树。 CN树与N树的数据分区方案的效率加入到相关对象的近似值,其中R树的最小边界矩形是据报道的最佳表现为范围搜索。我们展示我们如何在构造CN树中使用近似,然后,通过最小化距离计算并避免最小边界矩形的重叠来高效地支持该索引可以高效地查询范围。实验结果通过与众所周知R * -Tree的比较,表明所提出的CN树广泛地胜过R * -Tree作为内存索引,并且当用作磁盘内索引时,它会呈现竞争性表演。

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