Data Structures and Algorithms for Geographic Information Systems: Selected Topics

  • T. Ohler
  • P. Widmayer
Part of the International Centre for Mechanical Sciences book series (CISM, volume 347)


For the performance of geographic information systems, it has been widely recognized that efficient data structures and algorithms supporting proximity based access to spatial data are a crucial ingredient. Because of that, in the last years, a lot of data structures for storage of multidimensional non-point objects have been proposed in the literature (for an overview see our tutorial introduction in this volume). Almost all of these approaches are made to support queries based on spatial neighborhood of objects, so-called proximity queries. Common examples of such proximity queries are range queries, returning all objects intersecting a given (rectangular) region, or nearest neighbor queries, determining the object nearest to a given point. But a geographic or cartographic information system should also support queries that do not exclusively refer to the spatial neighborhood of objects. For example, different versions of spatial data must be organized such that proximity queries to any version of the data can be answered efficiently. Or, given a set of object classes, a spatial query that involves an arbitrary combination of these classes should also be supported. Because of the growing size of data sets to be handled in geographic information systems, an appropriate data organisation to support such and several other application-specific queries becomes more and more important. In this paper, we will present two approaches for access structures supporting the two types of queries mentioned above. Handling of versioned, spatial data is the topic of Section 2, while Section 3 discusses queries on some classes selected from the set of object classes stored in a geographic information system.


Data Space Range Query Data Block Access Structure Concurrency Control 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Katz, R.H.: Towards a unified framework for version modeling in engineering databases, ACM Computing Surveys, Vol. 22, No. 4, 1990, 375–408.CrossRefGoogle Scholar
  2. 2.
    Clifford, J. and G. Ariav: Temporal data management: models and systems, in: New directions for database systems (Eds. G. Ariav and J. Clifford ), Ablex Publishing Co., Norwood N.J. 1986, 168–186.Google Scholar
  3. 3.
    Bernstein, P.A., V. Hadzilacos and N. Goodman: Concurrency control and recovery in database systems, Addison Wesley Publ. Co., Reading 1987.Google Scholar
  4. 4.
    Barghouti, N.S. and G.E. Kaiser: Concurrency control in advanced database applications, ACM Computing Surveys, Vol. 23, No. 3, 1991, 269–317.CrossRefGoogle Scholar
  5. 5.
    Driscoll, J.R., N. Sarnak, D.D. Sleator and R.E. Tarjan: Making data structures persistent, Journal of Comp. and System Sci., Vol. 38, 1989, 86–124.CrossRefzbMATHMathSciNetGoogle Scholar
  6. 6.
    Lomet, D. and B. Salzberg: Access methods for multiversion data, ACM SIGMOD International Conference on Management of Data, 1989, 315–324.Google Scholar
  7. 7.
    Becker, B., S. Gschwind, T. Ohler, B. Seeger and P. Widmayer: On optimal multiversion access structures, Proc. 3rd International Symposium on Large Spatial Databases, Springer Lecture Notes in Computer Science, Vol. 692, Springer-Verlag, Berlin 1993, 123–141.Google Scholar
  8. 8.
    Kolovson, C. and M. Stonebraker: Indexing techniques for historical databases, 5th IEEE International Conference on Data Engineering, 1989, 127–137.Google Scholar
  9. 9.
    Lomet, D. and B. Salzberg: The performance of a multiversion access method, ACM SIGMOD International Conference on Management of Data, 1990, 353–363.Google Scholar
  10. 10.
    Easton, M.: Key-sequence data sets on indelible storage, IBM J. Res. Development, Vol. 30, No. 3, 1986, 230–241.Google Scholar
  11. 11.
    Lanka, S. and E. Mays: Fully persistent Bk—trees, ACM SIGMOD International Conference on Management of Data, 1991, 426–435.Google Scholar
  12. 12.
    Ohler, T.: The Multi Class Grid File: An Access Structure for Multi Class Range Queries, Proc. 5th International Symposium on Spatial Data Handling, Charleston, 1992, 260–271.Google Scholar
  13. 13.
    Pagel, B.-U., H.-W. Six and H. Toben: The transformation technique for spatial objects revisited, Proc. 3rd International Symposium on Large Spatial Databases, Springer Lecture Notes in Computer Science, Vol. 692, Springer-Verlag, Berlin 1993, 73–88.Google Scholar
  14. 14.
    Nievergelt, J., H. Hinterberger and K.C. Sevcik: The grid file: an adaptable, symmetric multikey file structure, ACM Transactions on Database Systems Vol. 9, No. 1, 1984, 38–71.CrossRefGoogle Scholar
  15. 15.
    Freeston, M.W.: The BANG-file: a new kind of grid file, ACM SIGMOD International Conference on Management of Data, 1987, 260–269.Google Scholar
  16. 16.
    Krishnamurthy, R. and K.-Y. Whang: Multilevel Grid Files, IBM Research Report, Yorktown Heights 1985.Google Scholar

Copyright information

© Springer-Verlag Wien 1994

Authors and Affiliations

  • T. Ohler
    • 1
  • P. Widmayer
    • 1
  1. 1.ETH ZentrumZürichSwitzerland

Personalised recommendations