Geological tetrahedral model-oriented hybrid spatial indexing structure based on Octree and 3D R*-tree

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.

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The Study and Design of QR*-Tree Spatial Indexing Structure

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.182-183.2030 ◽

2012 ◽

Vol 182-183 ◽

pp. 2030-2034

Author(s):

Xiao Yu Song ◽

Yong Hui Wang ◽

Shou Jin Wang

Keyword(s):

Spatial Data ◽

Spatial Database ◽

Hybrid Structure ◽

Storage Space ◽

Spatial Indexing ◽

Indexing Structure ◽

Quad Tree ◽

Huge Data ◽

Data Volume

In this study, we will discuss a fast spatial indexing structure called QR*-tree based on R*-tree and quad-tree. Now, R*-tree and R-tree are widely used in spatial database as a spatial indexing structure, But for each algorithm alone, it is not suitable for the huge data volume. The hybrid structure that we proposed is composed of many R*-trees based on space partitioned by quad-tree. Although it demands more storage space than R*-tree or quad-tree, it gains better performance in insertion, deletion, and searching especially, and the more the amount of spatial data is, the better performance the hybrid-tree has.

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A spatial indexing structure based on object partition_ br-tree

2007 International Conference on Wavelet Analysis and Pattern Recognition ◽

10.1109/icwapr.2007.4420696 ◽

2007 ◽

Author(s):

Run-Tao Liu ◽

Zhong-Xiao Hao

Keyword(s):

Spatial Indexing ◽

Indexing Structure

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Multi Small Index (MSI): A spatial indexing structure

Journal of Information Science ◽

10.1177/0165551513483253 ◽

2013 ◽

Vol 39 (5) ◽

pp. 643-660 ◽

Cited By ~ 6

Author(s):

Amer F. Al-Badarneh ◽

Abdullah S. Al-Alaj ◽

Basel A. Mahafzah

Keyword(s):

Spatial Indexing ◽

Indexing Structure ◽

Small Index

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.201-203.194 ◽

2011 ◽

Vol 201-203 ◽

pp. 194-197

Author(s):

Dian Zhu Sun ◽

Yong Wei Sun ◽

Xin Cai Kang ◽

Yan Rui Li

Keyword(s):

Line Segment ◽

Nearest Neighbor ◽

Target Line ◽

Data Type ◽

Spatial Indexing ◽

K Nearest Neighbor ◽

Line Segments ◽

Nearest Neighbor Query ◽

Indexing Structure ◽

Hollow Ball

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.

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A Unified Indexing Strategy for the Mixed Data of a Future Marine GIS

Journal of Navigation ◽

10.1017/s0373463316000898 ◽

2017 ◽

Vol 70 (4) ◽

pp. 735-747

Author(s):

Tao Liu ◽

Xie Han ◽

Jie Yang ◽

Liqun Kuang

Keyword(s):

Big Data ◽

Information Systems ◽

Spatial Databases ◽

Three Dimensional ◽

Data Retrieval ◽

Mixed Data ◽

Spatial Indexing ◽

Data Types ◽

Indexing Structure ◽

Data Organisation

Spatial indexing technology is widely used in Geographic Information Systems (GIS) and spatial databases. As a data retrieval technology, spatial indexing is becoming increasingly important in the big-data age. The purpose of this study is to propose a unified indexing strategy for the mixed data of a future marine GIS. First, data organisation of the system is described. Second, the display condition of each type of data is introduced. These conditions are the basis for the construction of a unified indexing structure. Third, a unified indexing structure for mixed data is presented. The construction process and the search method of the indexing structure are described. Finally, we implement the indexing strategy in our system “Automotive Intelligent Chart Three-dimensional Electronic Chart Display and Information Systems” (AIC 3D ECDIS). Our strategy can provide fast and integrated data retrieval. The spatial indexing strategy we propose breaks through the limitation of data types in our system. It can also be applied in other GIS systems. With the advent of the big-data age, mixed data indexing will become more and more important.

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