Quad-splitting algorithm for a window query on a Hilbert curve

Chen-Chang Wu, Ye-In Chang
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引用次数: 5

Abstract

Space-filling curves, particularly, Hilbert curves, have been extensively used to maintain spatial locality of multi-dimensional data in a wide variety of applications. A window query is an important query operation in spatial (image) databases. Given a Hilbert curve, a window query reports its corresponding orders without the need to decode all the points inside this window into the corresponding Hilbert orders. Given a query window of size p times q on a Hilbert curve of size T times T , Chung et al. have proposed an algorithm for decomposing a window into the corresponding Hilbert orders, which needs O ( n log T ) time, where n = max ( p , q ). By employing the properties of Hilbert curves, the authors present an efficient algorithm, named as Quad-Splitting, for decomposing a window into the corresponding Hilbert orders on a Hilbert curve without individual sorting and merging steps. Although the proposed algorithm also takes O ( n log T ) time, it does not perform individual sorting and merging steps which are needed in Chung et al. 's algorithm. Therefore experimental results show that the Quad-Splitting algorithm outperforms Chung et al. 's algorithm.
希尔伯特曲线上窗口查询的四分割算法
空间填充曲线,特别是希尔伯特曲线,在各种应用中被广泛用于保持多维数据的空间局部性。窗口查询是空间(图像)数据库中一个重要的查询操作。给定一条希尔伯特曲线,窗口查询报告其相应的阶数,而不需要将窗口内的所有点解码为相应的希尔伯特阶数。给定大小为T * T的Hilbert曲线上大小为p * q的查询窗口,Chung等人提出了一种将窗口分解为相应的Hilbert阶的算法,该算法需要O (n log T)时间,其中n = max (p, q)。利用希尔伯特曲线的性质,作者提出了一种有效的算法,称为四分法,该算法将一个窗口分解为希尔伯特曲线上相应的希尔伯特阶,而不需要单独的排序和合并步骤。虽然所提出的算法也需要O (n log T)的时间,但它不执行Chung等人所需要的单独排序和合并步骤。的算法。因此,实验结果表明,四分频算法优于Chung等人。的算法。
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