Line Segment Nearest Neighbor Query of Spatial Database Based on R*S-Tree

2011 ◽  
Vol 201-203 ◽  
pp. 194-197
Author(s):  
Dian Zhu Sun ◽  
Yong Wei Sun ◽  
Xin Cai Kang ◽  
Yan Rui Li

An algorithm for nearest neighbor query of Line Segment based on the R*S-tree is proposed. The dynamic spatial indexing structure for spatial line segments was constructed based on the R*S-tree, and the k-nearest neighbor of the target line segment were obtained by the hollow ball. The distance between the target line segment and the neighbor line segments was computed, and the neighbor line segments were sorted by the distance. The result shows that the algorithm can obtain nearest neighbor line segment accurately and effectively and has the strong adaptability of data type.

Author(s):  
Yunguo Guan ◽  
Rongxing Lu ◽  
Yandong Zheng ◽  
Jun Shao ◽  
Guiyi Wei

2021 ◽  
pp. 100428
Author(s):  
Polychronis Velentzas ◽  
Michael Vassilakopoulos ◽  
Antonio Corral

2021 ◽  
Vol 11 (20) ◽  
pp. 9581
Author(s):  
Wei Wang ◽  
Yi Zhang ◽  
Genyu Ge ◽  
Qin Jiang ◽  
Yang Wang ◽  
...  

The spatial index structure is one of the most important research topics for organizing and managing massive 3D Point Cloud. As a point in Point Cloud consists of Cartesian coordinates (x,y,z), the common method to explore geometric information and features is nearest neighbor searching. An efficient spatial indexing structure directly affects the speed of the nearest neighbor search. Octree and kd-tree are the most used for Point Cloud data. However, Octree or KD-tree do not perform best in nearest neighbor searching. A highly balanced tree, 3D R*-tree is considered the most effective method so far. So, a hybrid spatial indexing structure is proposed based on Octree and 3D R*-tree. In this paper, we discussed how thresholds influence the performance of nearest neighbor searching and constructing the tree. Finally, an adaptive way method adopted to set thresholds. Furthermore, we obtained a better performance in tree construction and nearest neighbor searching than Octree and 3D R*-tree.


2020 ◽  
Vol 13 (6) ◽  
pp. 2324-2333
Author(s):  
Huijuan Lian ◽  
Weidong Qiu ◽  
Di Yan ◽  
Zheng Huang ◽  
Peng Tang

Sign in / Sign up

Export Citation Format

Share Document