期刊文献+

利用地球立体剖分格网生成Subdivision R-树索引模型 被引量:1

Subdivision R-Tree Index Model of the Earth-based Three-dimensional Subdivision Grids
原文传递
导出
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%。 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%.
出处 《武汉大学学报(信息科学版)》 EI CSCD 北大核心 2016年第4期443-449,共7页 Geomatics and Information Science of Wuhan University
基金 高分辨率对地观测系统国家重大专项(30-Y30B13-9003-14/16 03-Y30B06-9001-13/15) 广西自然科学基金(2012GXNSFAA053181 2013GXNSFBA019265 2013GXNSFBA019266)~~
关键词 空间索引 SUBDIVISION R-树 GeoSOT 八叉树 spatial index Subdivision R-tree subdivision GeoSOT octree
  • 相关文献

参考文献20

  • 1陈述彭.遥感地学分析的时空维[J].遥感学报,1997,1(3):161-171. 被引量:59
  • 2李国杰,程学旗.大数据研究:未来科技及经济社会发展的重大战略领域——大数据的研究现状与科学思考[J].中国科学院院刊,2012,27(6):647-657. 被引量:1606
  • 3Finkel R A , Bentley J L. Quad Trees a Data Struc- ture for Retrieval on Composite Keys[J], Acta In- formatica ,1974,4(1) : 1-9.
  • 4Guttman A. R-trees: A Dynamic Index Structure for Spatial Searching[J]. ACM, 1984,14(2) :47-57.
  • 5Sellis T, Roussopoulos N, Faloutsos C. The R+- tree: A Dynamic Index for Multi-dimensional Ob- jects lOLl. http://repository, cmu. edu/cgi/view- content, cgi? article = 1563b-context = compsci,2015.
  • 6Beckmann N, Kriegel H P, Schneider R, et al. The R -tree: an Efficient and Robust Access Method for Points and Rectangles[J]. ACM, 1990,19(2) : 322-331.
  • 7Kamel I, Faloutsos C. Hilbert R-tree: An Im- proved R-tree Using Fractals[OL]. http://drum. lib. umd. edu/handle/1903/5366,1993.
  • 8Kothuri R K V, Ravada S, Abugov D. Quadtree and R-tree Indexes in Oracle Spatial: A Comparison Using GIS Data[C]. The 2002 ACM SIGMOD In- ternational Conference on Management of Data, Wisconsin, USA, 2002.
  • 9郭菁,郭薇,胡志勇.大型GIS空间数据库的有效索引结构QR-树[J].武汉大学学报(信息科学版),2003,28(3):306-310. 被引量:30
  • 10FuYC, Hu Z Y, Guo W, et al. QR-tree. a Hy- brid Spatial Index Structure[C]. Machine Learning and Cybernetics, 2003 International Conference, Xi'an, China, 2003.

二级参考文献126

共引文献1940

同被引文献11

引证文献1

二级引证文献2

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部