Abstract
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.
Highlights
A region query operation is often involved in many processes, e.g., spatial analysis, data sharing, and visualization and mapping [1]
In an exact region query, the candidate features retrieved by a spatial index in the filter step must be further examined in the refinement step
The number of candidate features to be eliminated is reduced by limiting them to those whose minimum bounding rectangle (MBR) contains the corners of a query region
Summary
A region query operation is often involved in many processes, e.g., spatial analysis, data sharing, and visualization and mapping [1]. A common approach [4] to accelerate the region query when using a spatial index is to use an approximation, usually a minimum bounding rectangle (MBR), instead of the actual geometry for the intersecting test. This configuration may yield inaccurate results, in which the retrieved features (referred to as candidate features hereafter) may not exactly intersect with the query region. The actual performance of a distributing spatial index is highly related to the operating environment—for example, the number of nodes or the computing capability per node—and involves a performance ceiling for given hardware.
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