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Size Estimation of the Intersection Join between Two Line Segment Datasets

  • Enrico Nardelli
  • Guido Proietti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1884)

Abstract

In this paper we provide a theoretical framework for estimating the size of the intersection join between two line segment datasets (e.g., roads, railways, utilities). For real datasets, it has been pointed out that the line segment lengths and slopes are distributed according to specific mathematical laws [14]. Starting from this result, we show how to predict the size of the intersection join between two line segment datasets. We evaluate our formula through several experimentations, showing that the estimation is accurate, as compared to that obtained by using a naive uniform model.

Keywords

Line Segment Real Dataset Uniform Model Complementary Cumulative Distribution Function Spatial Dataset 
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.

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References

  1. 1.
    A. Belussi and C. Faloutsos. Estimating the selectivity of spatial queries using the’ correlation’ fractal dimension. In 21th Conference on Very Large Data Bases (VLDB’95), pages 299–310, Zurich, Switzerland, 1995.Google Scholar
  2. 2.
    T. Brinkhoff, H.P. Kriegel, and B. Seeger. Efficient processing of spatial joins using R-trees. In 19th ACM Int. Conf. on Management of Data (SIGMOD’93), pages 237–246, 1993.Google Scholar
  3. 3.
    S. Christodoulakis. Implication of certain assumptions in database performance evaluation. ACM TODS, 9(2):163–186, June 1984.Google Scholar
  4. 4.
    C. Faloutsos, M. Ranganathan, and Y. Manolopoulos. Fast subsequence matching in time-series databases. In 20th ACM Int. Conference on Management of Data (SIGMOD’94), pages 419–429, Minneapolis, MN, May 1994.Google Scholar
  5. 5.
    L. Forlizzi, R.H. Güting, E. Nardelli, and M. Schneider. A data model and data structures for moving objects databases. In 26th ACM Int. Conf. on Management of Data (SIGMOD 2000), pages 319–330, 2000.Google Scholar
  6. 6.
    A.U. Frank, S. Grumbach, R.H. Güting, C.S. Jensen, M. Koubarakis, N.A. Lorentzos, Y. Manolopoulos, E. Nardelli, B. Pernici, H.J. Schek, M. Scholl, T.K. Sellis, B. Theodoulidis, and P. Widmayer. Chorochronos: A research network for spatiotemporal database systems. SIGMOD Record, 28(3):12–21, 1999.CrossRefGoogle Scholar
  7. 7.
    V. Gaede and O. Günther. Multidimensional access methods. Computing Surveys, 30(2):170–231, 1998.CrossRefGoogle Scholar
  8. 8.
    V. Gaede and W.F. Riekert. Spatial access methods and query processing in the object-oriented GIS GODOT. In AGDM’94 Workshop, pages 40–52, Delft, The Netherlands, 1994.Google Scholar
  9. 9.
    R.L. Graham, D.E. Knuth, and O. Patashnik. Concrete Mathematics. Addison-Wesley Publishing Company, New York, 1989.zbMATHGoogle Scholar
  10. 10.
    R.H. Güting. An introduction to spatial database systems. VLDB Journal, 3(4):357–399, 1994.CrossRefGoogle Scholar
  11. 11.
    N. Koudas and K.C. Sevcik. Size separation spatial join. In 23th ACM Int. Conf. on Management of Data (SIGMOD’97), pages 324–335, 1997.Google Scholar
  12. 12.
    M.L. Lo and C.V. Ravishankar. Spatial joins using seeded trees. In 20th ACM Int. Conf. on Management of Data (SIGMOD’94), pages 209–220, 1994.Google Scholar
  13. 13.
    D. Papadias, N. Mamoulis, and Y. Theodoridis. Processing and optimization of multiway spatial joins using R-trees. In 18th ACM Symp. on Principles of Database Systems (PODS’99), pages 44–55, 1999.Google Scholar
  14. 14.
    G. Proietti and C. Faloutsos. Selectivity estimation of window queries for line segment datasets. In 7th ACM Conference on Information and Knowledge Management (CIKM’98), pages 340–347, Washington, DC, 1998.Google Scholar
  15. 15.
    G. Proietti and C. Faloutsos. Accurate modeling of region data. IEEE Trans. on Knowledge and Data Engineering, in press, 2000. Also available as CMU-TR-98-126, Dept. of Computer Science, Carnegie Mellon University, Pittsburgh, PA.Google Scholar
  16. 16.
    G.K. Zipf. Human behavior and principle of least effort: an introduction to human ecology. Addison Wesley, Cambridge, MA, 1949.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Enrico Nardelli
    • 1
    • 2
  • Guido Proietti
    • 1
    • 2
  1. 1.Dipartimento di Matematica Pura ed ApplicataUniversità di L’AquilaL’AquilaItaly
  2. 2.Consiglio Nazionale delle RicercheIstituto di Analisi dei Sistemi e InformaticaRomaItaly

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