Journal of Intelligent Information Systems

, Volume 31, Issue 1, pp 35–52 | Cite as

Using B+-trees for processing of line segments in large spatial databases

  • Hung-Yi LinEmail author


Points, lines, and regions are the three basic entities for constituting vector-based objects in spatial databases. Many indexing methods (G-tree, K-D-B tree, Quad-tree, PMR-tree, Grid-file, R-tree, and so on) have been widely discussed for handling point or region data. These traditional methods can efficiently organize point or region objects in a space into a hashing or hierarchical directory. They provide efficient access methods to meet the requirement of accurate retrievals. However, two problems are encountered when their techniques are applied to deal with line segments. The first is that representing line segments by means of point or region objects cannot exactly and properly preserve the spatial information about the proximities of line segments. The second problem is derived from the large dead space and overlapping areas in external and internal nodes of the hierarchical directory caused by the use of rectangles to enclose line objects. In this paper, we propose an indexing structure for line segments based on B + -tree to remedy these two problems. Through the experimental results, we demonstrate that our approach has significant improvement over the storage efficiency. In addition, the retrieval efficiency has also been significantly prompted as compared to the method using R-tree index scheme. These improvements derive mainly from the proposed data processing techniques and the new indexing method.


Spatial database Line segment-based database B+-trees GIS Line segments 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Bentley, J. L. (1975). Multidimensional binary search trees used for associative searching. Communications of the ACM, 18(9), 509–517.zbMATHCrossRefMathSciNetGoogle Scholar
  2. Blanken, H., Ijbema, A., Meek, P., & Akker, B. (1990). The generalized grid file: Description and performance aspects. In Proceeding of 6th IEEE International Conference on Data Engineering, 380–388. Washington, DC: IEEE Computer Society.Google Scholar
  3. Gaede, V., & Gunther, O. (1998). Multidimensional access methods. ACM Computing Surveys, 30(2), 170–231.CrossRefGoogle Scholar
  4. Guttman, A. (1984). R-trees: A dynamic index structure for spatial searching. In Proceedings of ACM SIGMOD (47–57). New York: ACM.Google Scholar
  5. Hoel, E. G., & Samet, H. (1991). Efficient processing of spatial queries in line segment databases In O. Gunther & H. J. Schek (Eds.), Advances in spatial databases—2nd symposium, SSD91, Lecture notes in computer science 525 (pp. 237–256). Berlin: Springer.Google Scholar
  6. Hoel, E. G., & Samet, H. A. (1992). Qualitative comparison study of data structure for large segment databases, SIGMOD (pp. 205–214). San Diego, CA.Google Scholar
  7. Jagadish, H. V. (1990). On indexing line segments. In D. McLeod, R. Sacks-Davis, & H. Schek (Eds.), Proceedings of the Sixteen International Conference on Very Large Data Bases (614–625). Brisbane, Australia.Google Scholar
  8. Kumar, A. (1994). G-tree: A new data structure for organization multidimensional data. IEEE Transaction on Knowledge and Data Engineering, 6(2), 341–347.CrossRefGoogle Scholar
  9. Lanka, S., & Mays, E. (1991). Fully persistent B +-trees. In Proceedings of ACM SIGMOD (426–435). New York: ACM.Google Scholar
  10. Lindenbaum, M., Samet, H., & Hjaltason, G. R. A. (2000). Probabilistic analysis of trie-based sorting of large collections of line segments in spatial databases, University of Maryland Computer Science TR 3455.1.Google Scholar
  11. Nievergelt, J., Hinterberger, H., & Sevcik, K. (1984). The grid file: an adaptable symmetric multikey file structure. ACM Transactions on Database Systems, 9(1), 38–71.CrossRefGoogle Scholar
  12. Orenstein, J. A., & Merrett, T. H. (1984). A class of data structure for associative searching. In Proceedings of the Third ACM SIGACT-SIGMOD Symp. on Principles of Database Systems (pp. 181–190). New York: ACM.Google Scholar
  13. Papadias, D., & Theodoridis, Y. (1997). Spatial relations, minimum bounding rectangles and spatial data structures. International Journal of Geographical Information Science, 11(2), 111–138.CrossRefGoogle Scholar
  14. Robinson, J. T. (1981). The K-D-B Tree: A search structure for large multidimensional dynamic indexes. In Proceedings of ACM SIGMOD (pp. 10–18). New York: ACM.Google Scholar
  15. Six, H., & Widmayer, P. (1988). Spatial searching in geometric databases. In Proceeding of 4th IEEE International Conference on Data Engineering (496–503).Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.Department of Logistics Engineering & ManagementTaichung Institute of TechnologyTaichungChina

Personalised recommendations