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)

Abstract

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.

Keywords

Spatial Access Methods R-trees Quadtrees Query Processing 

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References

  1. 1.
    Beckmann, N., Kriegel, H.P., Schneider, R., Seeger, B.: The R*-tree: an Efficient and Robust Access Method for Points and Rectangles. In: SIGMOD Conference, pp. 322-331 (1990)Google Scholar
  2. 2.
    Chen, Y., Patel, J.M.: Efficient Evaluation of All-Nearest-Neighbor Queries. In: ICDE Conference, pp. 1056-1065 (2007)Google Scholar
  3. 3.
    Comer, D.: The Ubiquitous B-tree. ACM Computing Surveys 11(2), 121–137 (1979)MathSciNetCrossRefMATHGoogle Scholar
  4. 4.
    Gaede, V., Gunther, O.: Multidimensional Access Methods. ACM Computing Surveys 30(2), 170–231 (1998)CrossRefGoogle Scholar
  5. 5.
    Gorawski, M., Bugdol, M.: New Trends in Data Warehousing and Data Analysis. In: Kozielski, S., Wrembel, R. (eds.) Cost Model for XBR-tree. Springer, Heidelberg (2009)Google Scholar
  6. 6.
    Guttman: R-trees: A Dynamic Index Structure for Spatial Searching. In: SIGMOD Conference, pp. 47-57 (1984)Google Scholar
  7. 7.
    Hjaltason, G.R., Samet, H.: Ranking in Spatial Databases. In: Egenhofer, M.J., Herring, J.R. (eds.) SSD 1995. LNCS, vol. 951, pp. 83–95. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  8. 8.
    Hjaltason, G.R., Samet, H.: Distance Browsing in Spatial Databases. ACM Transactions on Database Systems 24(2), 265–318 (1999)CrossRefGoogle Scholar
  9. 9.
    Hoel, E.G., Samet, H.: A Qualitative Comparison Study of Data Structures for Large Line Segment Databases. In: SIGMOD Conference, pp. 205-214 (1992)Google Scholar
  10. 10.
    Hoel, E.G., Samet, H.: Benchmarking Spatial Join Operations with Spatial Output. In: VLDB Conference, pp. 606-618 (1995)Google Scholar
  11. 11.
    Kim, Y.J., Patel, J.: Performance Comparison of the R*-tree and the Quadtree for kNN and Distance Join Queries. IEEE Transactions on Knowledge and Data Engineering 22(7), 1014–1027 (2010)CrossRefGoogle Scholar
  12. 12.
    Kothuri, R.K., Ravada, S., Abugov, D.: Quadtree and R-tree Indexes in Oracle Spatial: A Comparison using GIS Data. In: SIGMOD Conference, pp. 546–557 (2002)Google Scholar
  13. 13.
    Manolopoulos, Y., Nanopoulos, A., Papadopoulos, A., Theodoridis, Y.: R-Trees: Theory and Applications. Springer, Heidelberg (2006)CrossRefMATHGoogle Scholar
  14. 14.
    Roumelis, G., Vassilakopoulos, M., Corral, A.: Algorithms for processing Nearest Neighbor Queries using xBR-trees. In: 15th Panhellenic Conference on Informatics (PCI 2011) (to appear 2011)Google Scholar
  15. 15.
    Roussopoulos, N., Kelley, S., Vincent, F.: Nearest Neighbor Queries. In: SIGMOD Conference, pp.71-79 (1995)Google Scholar
  16. 16.
    Samet, H.: Applications of Spatial Data Structures: Computer Graphics, Image Processing, and GIS. Addison-Wesley, Reading (1990)Google Scholar
  17. 17.
    Vassilakopoulos, M., Manolopoulos, Y.: External Balanced Regular (x-BR) Trees: New Structures for Very Large Spatial Databases. In: Advances in Informatics: Proc. 7th Hellenic Conf. on Informatics (HCI 1999), pp. 324–333. World Scientific Publ. Co., Singapore (2000)CrossRefGoogle Scholar

Copyright information

© 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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