Research on Hybrid Index Based on 3D Multi-Level Adaptive Grid and R+ Tree

• Published in: IEEE access Vol. 9; pp. 146010 - 146022
• Main Authors: Liu, Yongshan, Hao, Tianbao, Gong, Xiang, Kong, Dehan, Wang, Jianjun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Adaptive algorithm; Algorithms; Clustering algorithms; Datasets; Geographic information systems; hybrid index; Indexes; Massive data points; multilevel grid; Normal distribution; Periodic structures; Queries; R+ tree; Spatial data; spatial database; Spatial databases; Spatial indexes; Three-dimensional displays
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3115510

With the widespread application of Geographic Information System (GIS), three-dimensional spatial data, as the reflection of the real world entity, has an increasing amount of data, and the phenomenon of uneven data distribution appears. If a single spatial index structure is used to store and manage these data, there will be a waste of storage space and low query efficiency. A hybrid index structure based on 3D multi-level adaptive grid and R+ tree was proposed to solve these problems. The index structure was mainly composed of two structures, multi-level grid and R+ tree. Firstly, the data set was processed by the multi-level automatic grid algorithm based on normal distribution, and the length, width and height of the grid were obtained. Secondly, a multi-level adaptive grid structure was used to partition the data space quickly and effectively, and the advantage of zero overlap of the intermediate nodes of the R+ tree was used for efficient indexing. Finally, the maintenance and query algorithms of the index structure were given in detail, which solved the problem of low index establishment and retrieval efficiency under the condition of uneven distribution of massive data sets. In this paper, a data set subject to Gauss distribution was used to simulate the distribution of three-dimensional data. Through a large number of experimental comparison tests, it was proved that the hybrid index structure based on 3D multi-level adaptive grid-R+ tree proposed in this paper had good performance in both index structure construction and query in the case of massive data sets or uneven data distribution.