FA-Tree — A Dynamic Indexing Structure for Spatial Data

  • Chin-Chen Chang
  • Jau-Ji Shen
  • Yung-Chen Chou
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 29)


Non-standard database applications such as CAD/CAM or geographic information processing are becoming increasingly important. Such application systems must be equipped with the capability of effective accessibility to spatial data. The spatial domain consists of many spatial objects that are made up of points, lines, regions, and even high dimensional data. In order to effectively manipulate the spatial data, the tree structure is applied. In this paper, we consider such problems as spatial data retrieval, dynamic manipulation and storage utilization by indexing the large spatial data. A new tree structure, Five- Area Tree (denotes to FA-Tree), is proposed to organize the spatial data. Also, our experimental results show that the FA-Tree has better storage utilization than the Nine-Area Tree (also known as the NA-Tree).


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    N. Beckmann, H. P. Kriegel, R. Schneider and B. Seeger, “The R*-Tree: An Efficient and Robust Access Method for Points and Rectangles,” In Proceedings of the ACM SIGMOD International Conference on Management of Data, 1990, pp. 322–331.Google Scholar
  2. [2]
    Y. I. Chang, C. H. Liao and H. L. Chen, “NA-Trees: A Dynamic Index for Spatial Data,” Journal of Information Science and Engineering, Vol. 19, 2003, pp. 103–139.MathSciNetGoogle Scholar
  3. [3]
    C. Faloutsos and I. Kamel, “Beyond Uniformity and Independence: Analysis of R-trees Using the Concept of Fractal Dimension,” In Proceedings of the 13 th ACM Symposium on Principles of Database Principles, 1994, pp. 4–13.Google Scholar
  4. [4]
    V. Gaede and O. Günther, “Multidimensional Access Methods,” ACM Computing Surveys, Vol. 30, No. 2, 1998, pp. 170–231.CrossRefGoogle Scholar
  5. [5]
    O. Gunther and J. Bilmes, “Tree-Based Access Methods for Spatial Databases: Implementation and Performance Evaluation,” IEEE Transactions on Knowledge and Data Engineering, Vol. 3, 1991, pp. 342–356.CrossRefGoogle Scholar
  6. [6]
    A. Guttman, “R-Trees: A Dynamic Index Structure for Spatial Searching,” In Proceedings of the ACM SIGMOD International Conference on Management of Data, 1984, pp. 47–57.Google Scholar
  7. [7]
    A. Kumar, “G-Tree: A New Data Structure for Organizing Multidimensional Data,” IEEE Transactions on Knowledge and Data Engineering, Vol. 6, 1994, pp. 341–347.CrossRefGoogle Scholar
  8. [8]
    S. T. Leutenegger and M. A. Lopez, “The Effect of Buffering on the Performance of R-Trees,” In Proceedings of the 14 th International Conference on Data Engineering, 1998, pp. 164–171.Google Scholar
  9. [9]
    K. J. Li and R. Laurini, “The Spatial Locality and a Spatial Indexing Method by Dynamic Clustering in Hypermap System,” In Proceedings of the 2 nd Symposium Large Spatial Databases, 1991, pp. 207–223.Google Scholar
  10. [10]
    Y. Ohsawa and M. Sakauchi, “A New Tree Type Data Structure with Homogeneous Nodes Suitable for a Very Large Spatial Database,” In Proceedings of the 6 th IEEE International Conference on Data Engineering, 1990, pp. 296–303.Google Scholar
  11. [11]
    T. Sellis, N. Roussopoulos and C. Faloutsos, “The R +-Tree: A Dynamic Index for Multi-Dimensional Objects,” In Proceedings of the 13 th VLDB Conference, 1987, pp. 507–518.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Chin-Chen Chang
    • 1
  • Jau-Ji Shen
    • 2
  • Yung-Chen Chou
    • 1
  1. 1.Department of Computer Science and Information EngineeringNational Chung Cheng University ChiayiTaiwan R.O.C.
  2. 2.Department of Information ManagementNational Huwei University of Science and Technology YunlinTaiwan R.O.C.

Personalised recommendations