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SQMR-tree: An Efficient Hybrid Index Structure for Large Spatial Data  

Shin, In-Su (건국대학교 컴퓨터공학과)
Kim, Joung-Joon (건국대학교 컴퓨터공학과)
Kang, Hong-Koo (한국인터넷진흥원(KISA) 인터넷침해대응센터 침해예방단)
Han, Ki-Joon (건국대학교 컴퓨터공학과)
Publication Information
Spatial Information Research / v.19, no.4, 2011 , pp. 45-54 More about this Journal
Abstract
In this paper, we propose a hybrid index structure, called the SQMR-tree(Spatial Quad MR-tree) that can process spatial data efficiently by combining advantages of the MR-tree and the SQR-tree. The MR-tree is an extended R-tree using a mapping tree to access directly to leaf nodes of the R-tree and the SQR-tree is a combination of the SQ-tree(Spatial Quad-tree) which is an extended Quad-tree to process spatial objects with non-zero area and the R-tree which actually stores spatial objects and are associated with each leaf node of the SQ-tree. The SQMR-tree consists of the SQR-tree as the base structure and the mapping trees associated with each R-tree of the SQR-tree. Therefore, because spatial objects are distributedly inserted into several R-trees and only R-trees intersected with the query area are accessed to process spatial queries like the SQR-tree, the query processing cost of the SQMR-tree can be reduced. Moreover, the search performance of the SQMR-tree is improved by using the mapping trees to access directly to leaf nodes of the R-tree without tree traversal like the MR-tree. Finally, we proved superiority of the SQMR-tree through experiments.
Keywords
Large Spatial Data; Hybrid Index Structure; R-tree; SQMR-tree;
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Times Cited By KSCI : 5  (Citation Analysis)
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