引用本文: 翟卫欣, 程承旗, 童晓冲, 陈波. 利用地球立体剖分格网生成Subdivision R-树索引模型[J]. 武汉大学学报 ( 信息科学版), 2016, 41(4): 443-449.
ZHAI Weixin, CHENG Chengqi, TONG Xiaochong, CHEN Bo. Subdivision R-Tree Index Model of the Earth-based Three-dimensional Subdivision Grids[J]. Geomatics and Information Science of Wuhan University, 2016, 41(4): 443-449.
 Citation: ZHAI Weixin, CHENG Chengqi, TONG Xiaochong, CHEN Bo. Subdivision R-Tree Index Model of the Earth-based Three-dimensional Subdivision Grids[J]. Geomatics and Information Science of Wuhan University, 2016, 41(4): 443-449.

## Subdivision R-Tree Index Model of the Earth-based Three-dimensional Subdivision Grids

• 摘要: 针对三维数据管理中八叉树索引冗余多、R-树索引插入删除过程复杂的问题,依托GeoSOT地球立体剖分格网,提出了一种新的八叉树与R-树有机结合的Subdivision R-树索引模型(Subdivision R-tree)。首先,以GeoSOT地球立体剖分格网八叉树索引为基础构建了Subdivision R-树索引模型结构;随后,设计了Subdivision R-树索引模型基本的插入、删除、查询、分析算法;最后,开展了Subdivision R-树索引与原有数据索引性能对比试验,并对Subdivision R-树的阈值选取进行了相应分析。实验结果证明,Subdivision R-树的性能尤其是数据更新(插入、删除)等性能强于QR-树,随着数据分布的改变,性能提升更为明显,在数据分布较为集中的情况下,性能提升可达到20%。

Abstract: There are redundant complex issues concerning insertion and deletion processes in three-dimensional octree and R-tree index data management. Relying on GeoSOT Earth three-dimensional subdivision grids, we propose a new complex combination of the octree and R-tree indexes, the Subdivision R-tree model(Subdivision R-tree). First, GeoSOT three-dimensional subdivision octree-based grid index is used to construct a model Subdivision R-tree index structure. Subsequently, the basic design of the insertion, deletion, and query algorithm Subdivision R-tree index, is analyzed. Finally, we carry out a Subdivision R-tree indexing operation with the original data indexing performance comparison test, and discuss the threshold selection of Subdivision R-tree analysis accordingly. Test results show that the performance, especially Subdivision R-tree data update(insertionor deletion) process is better than octree. With the change of data distribution, the performance is more evident in the case that the data distribution is more concentrated, and the improvement is up to 20%.

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