Performance Comparison of xBR-trees and R*-trees for Single Dataset Spatial Queries

  • George Roumelis
  • Michael Vassilakopoulos
  • Antonio Corral
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6909)


Processing of spatial queries has been studied extensively in the literature. In most cases, it is accomplished by indexing spatial data by an access method. For queries involving a single dataset, like the Point Location Query, the Window (Distance Range) Query, the (Constrained) K Nearest Neighbor Query, the R*-tree (a data-driven structure) is a very popular choice of such a method. In this paper, we compare the performance of the R*-tree for processing single dataset spatial queries to the performance of a disk based structure that belongs to the Quadtree family, the xBR-tree (a space-driven structure). We demonstrate performance results (I/O efficiency and execution time) of extensive experimentation that was based on real datasets, using these two index structures. The winner depends on several parameters and the results show that the xBR-tree is a promising alternative for these spatial operations.


Spatial Access Methods R-trees Quadtrees Query Processing 


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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • George Roumelis
    • 1
  • Michael Vassilakopoulos
    • 2
    • 1
  • Antonio Corral
    • 3
  1. 1.Master in Information SystemsOpen University of CyprusCyprus
  2. 2.Dept. of Computer Science and Biomedical InformaticsUniversity of Central GreeceGreece
  3. 3.Dept. of Languages and ComputingUniversity of AlmeriaSpain

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