A new double sorting-based node splitting algorithm for R-tree

Abstract

A storing of spatial data and processing of spatial queries are important tasks for modern data bases. The execution efficiency of spatial query depends on underlying index structure. R tree is a well known spatial index structure. Currently there exist various versions of R tree, and one of the most common variations between them is node splitting algorithm. The problem of node splitting in one dimensional R tree may seem to be too trivial to be considered separately. One dimensional intervals can be split on the base of their sorting. Some of the node splitting algorithms for R tree with two or more dimensions comprise one dimensional split as their part. However, under detailed consideration, existing algorithms for one dimen sional split do not perform ideally in some complicated cases. This paper introduces a novel one dimensional node splitting algorithm based on two sortings that can handle such complicated cases better. Also this paper introduces node splitting algorithm for R tree with two or more dimensions that is based on the one dimen sional algorithm mentioned above. The tests show significantly better behavior of the proposed algorithms in the case of highly overlapping data.

R-SHT: A state history tree with R-Tree properties for analysis and visualization of highly parallel system traces

“…For instance, Double-Sorting [Korotkov 2012] offers the query performance of Guttman's quadratic split at the cost of the linear split. It searches for points whose coordinates can divide the node with minimal overlap.…”

SOLD: A node-Splitting algorithm for R-tree based on Objects’ Locations Distribution

“…The double sorting-based algorithm introduced in Korotkov [25] has two versions: first is for one-dimensional (1D) splitting only, the other is for two or more dimensional splitting. Both versions use separate arrays (two per dimension) to store lower and higher values of the objects’ MBRs.…”