Abstract
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.
This work was supported by the Korea Research Foundation Grant funded by the Korea Government(MOEHRD) (KRF-2005-041-D00665).
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Kim, J., Im, S., Kang, SW., Hwang, CS. (2006). Spatial Index Compression for Location-Based Services Based on a MBR Semi-approximation Scheme. In: Yu, J.X., Kitsuregawa, M., Leong, H.V. (eds) Advances in Web-Age Information Management. WAIM 2006. Lecture Notes in Computer Science, vol 4016. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11775300_3
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DOI: https://doi.org/10.1007/11775300_3
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