Abstract
Studies on spatial index, which is used for location-based services in mobile computing or GIS have increased in proportion to the increase in the spatial data. However, these studies were on the indices based on R-tree, and there are a few studies on how to increase the search performance of the spatial data by compressing MBRs. This study was conducted in order to propose a new MBR compression scheme, SA (Semi-approximation), and a SAR-tree that indexes spatial data using R-tree. The basic idea of this paper is the compression of MBRs in a spatial index. Since SA decreases the size of MBR keys, halves QMBR enlargement, and increases node utilization. Therefore, the SAR-tree heightens the overall search performance. The experiments show that the proposed index has increased performance, higher than that of the pre-established schemes on compression of MBRs.
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