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SQR-Tree : A Hybrid Index Structure for Efficient Spatial Query Processing  

Kang, Hong-Koo (한국인터넷진흥원(KISA) 인터넷침해대응센터 침해예방단)
Shin, In-Su (건국대학교 컴퓨터공학과)
Kim, Joung-Joon (건국대학교 컴퓨터공학과)
Han, Ki-Joon (건국대학교 컴퓨터공학과)
Publication Information
Spatial Information Research / v.19, no.2, 2011 , pp. 47-56 More about this Journal
Abstract
Typical tree-based spatial index structures are divided into a data-partitioning index structure such as R-Tree and a space-partitioning index structure such as KD-Tree. In recent years, researches on hybrid index structures combining advantages of these index structures have been performed extensively. However, because the split boundary extension of the node to which a new spatial object is inserted may extend split boundaries of other neighbor nodes in existing researches, overlaps between nodes are increased and the query processing cost is raised. In this paper, we propose a hybrid index structure, called SQR-Tree that can support efficient processing of spatial queries to solve these problems. SQR-Tree is a combination of SQ-Tree(Spatial Quad- Tree) which is an extended Quad-Tree to process non-size spatial objects and R-Tree which actually stores spatial objects associated with each leaf node of SQ-Tree. Because each SQR-Tree node has an MBR containing sub-nodes, the split boundary of a node will be extended independently and overlaps between nodes can be reduced. In addition, a spatial object is inserted into R-Tree in each split data space and SQ-Tree is used to identify each split data space. Since only R-Trees of SQR-Tree in the query area are accessed to process a spatial query, query processing cost can be reduced. Finally, we proved superiority of SQR-Tree through experiments.
Keywords
Large Spatial Data; Hybrid Index Structure; SQ-Tree; R-Tree;
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Times Cited By KSCI : 3  (Citation Analysis)
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