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Compacting Discriminator Information for Spatial Trees

机译:压缩空间树的鉴别器信息

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摘要

Cache-conscious behaviour of data structures becomes more important as memory sizes increase and whole databases fit into main memory. For spatial data, R-trees, originally designed for disk-based data, can be adopted for in-memory applications. In this paper, we will investigate how the small amount of space in an in-memory R-tree node can be used better to make R-trees more cache-conscious. We observe that many entries share sides with their parents, and introduce the partial R-tree which only stores information that is not given by the parent node. Our experiments showed that the partial R-tree shows up to 30 per cent better performance than the traditional R-tree. We also investigated if we could improve the search performance by storing more descriptive information instead of the standard minimum bounding box without decreasing the fanout of the R-tree. The partial static O-tree is based on the O-tree, but stores only the most important part of the information of an O-tree box. Experiments showed that this approach reduces the search time for line data by up to 60 per cent.
机译:随着内存大小的增加以及整个数据库适合主内存,数据结构的缓存意识行为变得越来越重要。对于空间数据,最初为基于磁盘的数据设计的R树可用于内存中的应用程序。在本文中,我们将研究如何更好地利用内存R-tree节点中的少量空间使R-tree更加关注缓存。我们观察到许多条目与其父级共享边,并引入了仅存储父级节点未提供信息的部分R树。我们的实验表明,部分R树的性能比传统R树高30%。我们还调查了是否可以通过存储更多描述性信息而不是标准最小边界框而不降低R树的扇形来提高搜索性能。部分静态O树基于O树,但仅存储O树框信息中最重要的部分。实验表明,这种方法最多可将线路数据的搜索时间缩短60%。

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