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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Kim, J., Im, S. J., Kang, S. W., Lee, S. H., & Hwang, C. S. (2006). MBR compression in spatial databases using semi-approximation scheme. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4251 LNAI-I, pp. 1124–1130). Springer Verlag. https://doi.org/10.1007/11892960_135
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