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首页> 外文期刊>The Computer journal >LR-tree: a Logarithmic Decomposable Spatial Index Method
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LR-tree: a Logarithmic Decomposable Spatial Index Method

机译：LR树：对数可分解空间索引方法

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摘要

Since its introduction in 1984, R-tree has been proven to be one of the most practical and well-behaved data structures for accommodating dynamic massive sets of geometric objects and conducting a very diverse set of queries on such datasets in real-world applications. This success has led to a variety of versions, each one trying to tune the performance parameters of the original proposal. Among them, the most prominent one is R~*-tree, which employs a number of carefully designed heuristics and is widely accepted as achieving the best performance in most cases. However, in the presence of actively changing datasets, R~*-tree still does not avoid performance tuning with forced reinsertion, i.e. a process that performs a kind of local rebuilding. The latter fact has motivated the investigation of the adaptation of a known dynamization technique, based on carefully triggered local rebuildings, for converting static or semi-dynamic, main memory data structures to dynamic ones onto R~* -trees. In this paper, we present LR-trees, a new efficient scheme for dynamic manipulation of large datasets, which combines the search performance of the bulk-loaded R-trees with the updated performance of R~*-trees. Experimental results provide evidence on the latter statement and illustrate the superiority of the proposed method.
机译：自1984年问世以来，R树已被证明是最实用，行为最完善的数据结构之一，可容纳动态的大量几何对象集，并在实际应用中对此类数据集进行非常多样化的查询。这一成功导致了各种版本，每个版本都试图调整原始提案的性能参数。其中最突出的一个是R〜* -tree，它采用了许多精心设计的启发式方法，并在大多数情况下被认为是获得最佳性能的。但是，在存在活跃变化的数据集的情况下，R〜*树仍然无法避免通过强制重新插入进行性能调整，即执行某种本地重建的过程。后一事实促使人们研究基于已知触发的局部重建的，用于将静态或半动态的主存储器数据结构转换为动态的动态数据到R_ *树上的已知动态化技术的适应性。在本文中，我们提出了LR树，这是一种用于大型数据集的动态操纵的新有效方案，它将大容量R树的搜索性能与R〜*树的更新性能相结合。实验结果为后一种说法提供了证据，并说明了该方法的优越性。

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