QRB-tree Indexing: Optimized Spatial Index Expanding upon the QR-tree Index

ISPRS International Journal of Geo-Information

Jieqing Yu¹  Qi Chu²  Yi Wei²  Lixin Wu³ 

[1] NASG Key Laboratory of Land Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou 221116, China;School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China;School of Geosciences and Info-Physics, Central South University, Changsha 410083, China;

关键词: spatial indexing;    QRB-tree index;    QR-tree index;    region query;    rough query;    exact query;   

DOI  :  10.3390/ijgi10110727

来源: DOAJ

【 摘 要 】

Support for region queries is crucial in geographic information systems, which process exact queries through spatial indexing to filter features and subsequently refine the selection. Although the filtering step has been extensively studied, the refinement step has received little attention. This research builds upon the QR-tree index, which decomposes space into hierarchical grids, registers features to the grids, and builds an R-tree for each grid, to develop a new QRB-tree index with two levels of optimization. In the first level, a bucket is introduced in every grid in the QR-tree index to accelerate the loading and search steps of a query region for the grids within the query region. In the second level, the number of candidate features to be eliminated is reduced by limiting the features to those registered to the grids covering the corners of the query region. Subsequently, an approach for determining the maximal grid level, which significantly affects the performance of the QR-tree index, is proposed. Direct comparisons of time costs with the QR-tree index and geohash index show that the QRB-tree index outperforms the other two approaches for rough queries in large query regions and exact queries in all cases.

【 授权许可】

Unknown