Advertisement

Efficient processing of spatial queries in line segment databases

  • Erik G. Hoel
  • Hanan Samet
Query Processing
Part of the Lecture Notes in Computer Science book series (LNCS, volume 525)

Abstract

A study is performed of the issues arising in the efficient processing of spatial queries in large spatial databases. The domain is restricted to line segment databases such as those found in transportation networks and polygonal maps. Three classes of queries are identified. Those that deal with the line segments themselves, those that involve both the line segments and the space from which they are drawn (e.g., proximity queries), and those that involve attributes of the line segments. Handling the three types of queries requires that the line segments be stored implicitly using a bucketing approach on the space from which they are drawn. A number of bucketing approaches are examined and the PMR quadtree is chosen as the most suitable representation. Its storage and execution time requirements are evaluated in the context of finding the nearest line segment to a given point. This operation is shown to take time proportional to the splitting threshold (similar to the bucket capacity) and is independent of the density of the data. The evaluation uses the road networks in the data of the U.S. Bureau of the Census.

Keywords and phrases

large spatial databases spatial queries spatial access methods bucketing methods lines spatial indexing spatial data structures hierarchical data structures geographic information systems PMR quadtrees 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [Bure89]
    Bureau of the Census, tiger/Line precensus files: 1990 technical documentation, Bureau of the Census, Washington, DC, 1989.Google Scholar
  2. [Buch90]
    A. Buchmann, O. Günther, T.R. Smith, and Y.-F. Wang, eds., Design and Implementation of Large Spatial Databases, Lecture Notes in Computer Science No. 409, Springer-Verlag, Berlin, 1990Google Scholar
  3. [Come79]
    D. Comer, The ubiquitous b-tree, ACM Computing Surveys 11, 2(June 1979), 121–137.Google Scholar
  4. [Falo87]
    C. Faloutsos, T. Sellis, and N. Roussopoulos, Analysis of object oriented spatial access methods, Proceedings of the SIGMOD Conference, San Francisco, May 1987, 426–439.Google Scholar
  5. [Fole90]
    J. D. Foley, A. van Dam, S. K. Feiner, and J. F. Hughes, Computer Graphics: Principles and Practice, Second Edition, Addison-Wesley, Reading, MA, 1990.Google Scholar
  6. [Fran84]
    W. R. Franklin, Adaptive grids for geometric operations, Cartographica 21, 2&3(Summer & Autumn 1984), 160–167.Google Scholar
  7. [Free87]
    M. Freeston, The bang file: a new kind of grid file, Proceedings of the SIGMOD Conference, San Francisco, May 1987, 260–269.Google Scholar
  8. [Günt87]
    O. Günther, Efficient structures for geometric data management, Ph.D. dissertation, UCB/ERL M87/77, Electronics Research Laboratory, College of Engineering, University of California at Berkeley, Berkeley, CA, 1987 (Lecture Notes in Computer Science 337, Springer-Verlag, Berlin, 1988).Google Scholar
  9. [Gutt84]
    A. Guttman, r-trees: a dynamic index structure for spatial searching, Proceedings of the SIGMOD Conference, Boston, June 1984, 47–57.Google Scholar
  10. [Henr89]
    A. Henrich, H. W. Six, and P. Widmayer, The lsd tree: spatial access to multidimensional point and non-point data, Proceedings of the Fifteenth International Conference on Very Large Data Bases, P. M. G. Apers and G. Wiederhold, eds., Amsterdam, August 1989, 45–53.Google Scholar
  11. [Hinr83]
    K. Hinrichs and J. Nievergelt, The grid file: a data structure designed to support proximity queries on spatial objects, Proceedings of the WG'83 (International Workshop on Graphtheoretic Concepts in Computer Science), M. Nagl and J. Perl, eds., Trauner Verlag, Linz, Austria, 1983, 100–113.Google Scholar
  12. [Jaga90]
    H. V. Jagadish, On indexing line segments, Proceedings of the Sixteenth International Conference on Very Large Data Bases, D. McLeod, R. Sacks-Davis, and H. Schek, eds., Brisbane, Australia, August 1990, 614–625.Google Scholar
  13. [Nels86]
    R. C. Nelson and H. Samet, A consistent hierarchical representation for vector data, Computer Graphics 20, 4(August 1986), 197–206 (also Proceedings of the SIGGRAPH'86 Conference, Dallas, August 1986).Google Scholar
  14. [Nels87]
    R. C. Nelson and H. Samet, A population analysis for hierarchical data structures, Proceedings of the SIGMOD Conference, San Francisco, May 1987, 270–277.Google Scholar
  15. [Niev84]
    J. Nievergelt, H. Hinterberger, and K. C. Sevcik, The grid file: an adaptable, symmetric multikey file structure, ACM Transactions on Database Systems 9, 1(March 1984), 38–71.Google Scholar
  16. [Oren89]
    J. A. Orenstein, Redundnacy in spatial databases, Proceedings of the SIGMOD Conference, Portland, OR, June 1989, 294–305.Google Scholar
  17. [Oren84]
    J. A. Orenstein and T. H. Merrett, A class of data structures for associative searching, Proceedings of the Third ACM SIGACT-SIGMOD Symposium on Principles of Database Systems, Waterloo, Canada, April 1984, 181–190.Google Scholar
  18. [Peuq90]
    D. J. Peuquet and D. F. Marble, Arc/info: An example of a contemporary geographic information system, in Introductory Readings In Geographic Information Systems, D. F. Peuquet and D. F. Marble, eds., Taylor & Francis, London, 1990, 90–99.Google Scholar
  19. [Same90a]
    H. Samet, The Design and Analysis of Spatial Data Structures, Addison-Wesley, Reading, MA, 1990.Google Scholar
  20. [Same90b]
    H. Samet, Applications of Spatial Data Structures: Computer Graphics, Image Processing, and GIS, Addison-Wesley, Reading, MA, 1990.Google Scholar
  21. [Same85]
    H. Samet and R. E. Webber, Storing a collection of polygons using quadtrees, ACM Transactions on Graphics 4, 3(July 1985), 182–222.Google Scholar
  22. [Sant76]
    L. A. Santalo, Integral geometry and geometric probability, in Encyclopedia of Mathematics and its Applications, G. C. Rota, ed., Addison-Wesley, Reading, MA, 1976.Google Scholar
  23. [Shaf90]
    C. A. Shaffer, H. Samet, and R. C. Nelson, Quilt: a geographic information system based on quadtrees, International Journal of Geographical Information Systems 4, 2(April–June 1990), 103–131.Google Scholar
  24. [Seeg90]
    B. Seeger and H. P. Kriegel, The buddy-tree: an efficient and robust access method for spatial data base systems, Proceedings of the Sixteenth International Conference on Very Large Data Bases, D. McLeod, R. Sacks-Davis, and H. Schek, eds., Brisbane, Australia, August 1990, 590–601.Google Scholar
  25. [Ston86]
    M. Stonebraker, T. Sellis, and E. Hanson, An analysis of rule indexing implementations in data base systems, Proceedings of the First International Conference on Expert Database Systems, Charleston, SC, April 1986, 353–364.Google Scholar
  26. [Tamm81]
    M. Tamminen, The excell method for efficient geometric access to data, Acta Polytechnica Scandinavica, Mathematics and Computer Science Series No. 34, Helsinki, Finland, 1981.Google Scholar
  27. [Tamm82]
    M. Tamminen, Efficient spatial access to a data base, Proceedings of the SIGMOD Conference, Orlando, June 1982, 47–57.Google Scholar
  28. [Trop81]
    H. Tropf and H. Herzog, Multidimensional range search in dynamically balanced trees, Angewandte Informatik 23, 2(February 1981), 71–77.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • Erik G. Hoel
    • 1
    • 3
  • Hanan Samet
    • 2
  1. 1.Bureau of the CensusWashington, D.C.
  2. 2.Computer Science Department Center for Automation Research Institute for Advanced Computer StudiesUniversity of MarylandCollege Park
  3. 3.the Center for Automation Research at the University of MarylandUSA

Personalised recommendations