Spatial Indexing Based on the Semi-approximation Scheme of MBR

Abstract

Over the last few years, the increase in spatial data has led to more research on spatial indexing. Most studies, however, are based on adding or changing various options in R-tree, and few studies have focused on increasing search performance via minimum bounding rectangle (MBR) compression. In a spatial index, a greater number of node entries lowers tree heights and decreases the number of node accesses, thereby shrinking disk I/O. This study proposes a new MBR compression scheme using semi-approximation (SA) and SAR-tree, which indexes spatial data using R-tree. Since SA decreases the size of MBR keys, halves QMBR enlargement, and increases node utilization, it improves the overall search performance. This study mathematically analyzes the number of node accesses and evaluates the performance of SAR-tree using real location data. The results show that the proposed index performs better than existing MBR compression schemes.