The SR-tree: an index structure for high-dimensional nearest neighbor queries
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Optimizing multidimensional index trees for main memory access
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
CSIM19: CSIM19: a powerful tool for building system models
Proceedings of the 33nd conference on Winter simulation
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Compressing Relations and Indexes
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Spatial indexing of high-dimensional data based on relative approximation
The VLDB Journal — The International Journal on Very Large Data Bases
A spatial index using MBR compression and hashing technique for mobile map service
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
Hi-index | 0.00 |
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.