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
Spatial indexing of high-dimensional data based on relative approximation
The VLDB Journal — The International Journal on Very Large Data Bases
Location Based Services
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
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The increased need for spatial data for location-based services or geographical information systems (GISs) in mobile computing has led to more research on spatial indexing, such as R-tree. The R-tree variants approximate spatial data to a minimal bounding rectangle (MBR). Most studies are based on adding or changing various options in R-tree, while a few studies have focused on increasing search performance via MBR compression. This study proposes a novel MBR compression scheme that uses semi-approximation (SA) MBRs and SAR-tree. Since SA decreases the size of MBR keys, halves QMBR enlargement, and increases node utilization, it improves the overall search performance. This scheme decreases quantized space more than existing quantization schemes do, and increases the utilization of each disk allocation unit. 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.