Advertisement

GeoInformatica

, Volume 6, Issue 2, pp 153–180 | Cite as

A Framework for Generating Network-Based Moving Objects

  • Thomas Brinkhoff
Article

Abstract

Benchmarking spatiotemporal database systems requires the definition of suitable datasets simulating the typical behavior of moving objects. Previous approaches for generating spatiotemporal data do not consider that moving objects often follow a given network. Therefore, benchmarks require datasets consisting of such “network-based” moving objects. In this paper, the most important properties of network-based moving objects are presented and discussed. Essential aspects are the maximum speed and the maximum capacity of connections, the influence of other moving objects on the speed and the route of an object, the adequate determination of the start and destination of an object, the influence of external events, and time-scheduled traffic. These characteristics are the basis for the specification and development of a new generator for spatiotemporal data. This generator combines real data (the network) with user-defined properties of the resulting dataset. A framework is proposed where the user can control the behavior of the generator by re-defining the functionality of selected object classes. An experimental performance investigation demonstrates that the chosen approach is suitable for generating large data sets.

spatiotemporal databases moving objects data generation benchmarks 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    R.K. Ahuja, T.L. Magnanti, and J.B. Orlin. Network Flows: Theory, Algorithms, and Applications, Prentice-Hall, 1993.Google Scholar
  2. 2.
    T. Brinkhoff. “Requirements of traffic telematics to spatial databases,” in Proceedings 6th International Symposium on Large Spatial Databases, Hong Kong, China. Lecture Notes in Computer Science, Vol. 1651:365-369, 1999.Google Scholar
  3. 3.
    Bundesanstalt für Straßenwesen, Bundesrepublik Deutschland. http://www.bast.de/Google Scholar
  4. 4.
    J. Gray. The Benchmark Handbook, Morgan-Kaufman, 1991.Google Scholar
  5. 5.
    O. Günther, V. Oria, P. Picouet, and J.-M. Saglio, M. Scholl. “Benchmarking spatial joins á la carte,” in Proceedings 10th International Conference on Scientific and Statistical Database Management, Capri, Italy, pp. 32-41, 1998.Google Scholar
  6. 6.
    A. Guttman. “R-trees: A dynamic index structure for spatial searching,” in Proceedings ACM SIGMOD International Conference on Management of Data, Boston, MA, pp. 47-57, 1984.Google Scholar
  7. 7.
    Integrating Spatial and Temporal Databases, Seminar, Schloss Dagstuhl, Wadern, Germany, November 22–27, 1998.Google Scholar
  8. 8.
    H.-P. Kriegel, M. Schiwietz, R. Schneider, and B. Seeger. “Performance comparison of point and spatial access methods,” in Proceedings 1st Symposium on the Design and Implementation of Large Spatial Databases, Santa Barbara, CA. Lecture Notes in Computer Science, Vol. 409:89-114, 1989.Google Scholar
  9. 9.
    MapInfo Corporation. MapInfo Professional TM : Reference Manual, 1995.Google Scholar
  10. 10.
    H. Mehl. “Mobilfunk-Technologien in der Verkehrstelematik,” Informatik-Spektrum, Vol. 19:183-190, 1996.Google Scholar
  11. 11.
    Oracle Corporation. Oracle® Spatial, User's Guide and Reference, Release 8.1.7, 2000.Google Scholar
  12. 12.
    D. Pfoser and Y. Theodoridis. “Generating semantics-based trajectories of moving objects,” in Proceedings International Workshop on Emerging Technologies for Geo-Based Applications, Ascona, Switzerland, pp. 59-76, 2000.Google Scholar
  13. 13.
    Rand McNally. The Road Atlas: United States, Canada & Mexico, 2001.Google Scholar
  14. 14.
    J.-M. Saglio and J. Moreira. “Oporto: A realistic scenario generator for moving objects,” in Proceedings DEXA Workshop on Spatio-Temporal Data Models and Languages, Florence, Italy, pp. 426-432, 1999. An extended version is published in: GeoInformatica Vol. 5(1):71–93, 2001.Google Scholar
  15. 15.
    T. Sellis. “Research issues in spatio-temporal database systems,” in Proceedings 6th International Symposium on Large Spatial Databases, Hong Kong, China, Lecture Notes in Computer Science, Vol. 1651:5-11, 1999.Google Scholar
  16. 16.
    B. Seeger, M. Egenhofer, J. Gray, S. Leutenegger, and D. Papadias. “Seeking the truth—curses and blessings of experiments,” Panel of the 7th International Symposium on Spatial and Temporal Databases, 2001.Google Scholar
  17. 17.
    Spatial and Temporal Databases, 7th International Symposium, Redondo Beach, CA, July 12–15, 2001.Google Scholar
  18. 18.
    Spatio-Temporal Database Management, Workshop, Edinburgh, Scotland, September 10–11, 1999.Google Scholar
  19. 19.
    Spatio-Temporal Data Models and Languages, Workshop, Florence, Italy, August 30–31, 1999.Google Scholar
  20. 20.
    M. Stonebraker, J. Frew, K. Gardels, and J. Meredith. “The SEQUOIA 2000 storage benchmark,” in Proceedings ACM SIGMOD International Conference on Management of Data, Washington, DC, pp. 2-11, 1993.Google Scholar
  21. 21.
    Y. Theodoridos and M. Nascimento. “Generating spatiotemporal datasets on the WWW,” ACM SIGMOD Record, Vol. 29(3):39-43, 2000.Google Scholar
  22. 22.
    Y. Theodoridis, J.R.O. Silva, and M.A. Nascimento. “On the generation of spatiotemporal datasets,” in Proceedings 6th International Symposium on Large Spatial Databases, Hong Kong, China, Lecture Notes in Computer Science, Vol. 1651:147-164, 1999.Google Scholar
  23. 23.
    U.S. Government Information and Maps Department. The Map Collection—TIGER/Line Files, http://www.lib.ucdavis.edu/govdoc/MapCollection/tiger.html.Google Scholar
  24. 24.
    J. Zobel, A. Moffat, and K. Ramamohanarao. “Guidelines for presentation and comparison of indexing techniques,” ACM SIGMOD Record, Vol. 25(3):10-15, 1996.Google Scholar
  25. 25.
    3sat: Raus aus dem Stau, Ein nano-Schwerpunkt. http://www.3sat.de/nano/bstuecke/18130/index.html.Google Scholar

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Thomas Brinkhoff
    • 1
  1. 1.Institute of Applied Photogrammetry and Geoinformatics (IAPG)Fachhochschule Oldenburg/Ostfriesland/Wilhelmshaven (University of Applied Sciences)OldenburgGermany

Personalised recommendations