Advertisement

Improved approximate pattern matching on hypertext

  • Gonzalo Navarro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1380)

Abstract

The problem of approximate pattern matching on hypertext is defined and solved by Amir et al. in O(m(n logm+e)) time, where m is the length of the pattern, n is the total text size and e is the total number of edges. Their space complexity is O(mn). We present a new algorithm which is O(mk(n+e)) time and needs only O(n) extra space, where k < m is the number of allowed errors in the pattern. If the graph is acyclic, our time complexity drops to O(m(n+e)), improving Amir's results.

Keywords

Space Complexity Edit Distance Classical Algorithm Text Character Extra Space 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    T. Akutsu. A linear time pattern matching algorithm between a string and a tree. In Proc. CPM'93, pages 1–10, 1993.Google Scholar
  2. 2.
    A. Amir, M. Lewenstein, and N. Lewenstein. Pattern matching in hypertext. In Proc. WADS'97, LNCS 1272, pages 160–173, 1997.Google Scholar
  3. 3.
    R. Baeza-Yates and G. Navarro. A faster algorithm for approximate string matching. In Proc. CPM'96, LNCS 1075, pages 1–23, 1996. ftp://ftp.dcc.uchile.cl/-pub/users/gnavarro/cpm96.ps.gz.MathSciNetGoogle Scholar
  4. 4.
    R. Baeza-Yates and C. Perleberg. Fast and practical approximate pattern matching. In Proc. CPM'92, LNCS 644, pages 185–192, 1992.Google Scholar
  5. 5.
    W. Chang and J. Lampe. Theoretical and empirical comparisons of approximate string matching algorithms. In Proc. CPM'92, LNCS 644, pages 172–181, 1992.MathSciNetGoogle Scholar
  6. 6.
    J. Conklin. Hypertext: An introduction and survey. IEEE Computer, 20(9):17–41, September 1987.Google Scholar
  7. 7.
    G. Das, R. Fleischer, L. Gasieniec, D. Gunopulos, and J. Karkäinen. Episode matching. In Proc. CPM'97, LNCS 1264, pages 12–27, 1997.Google Scholar
  8. 8.
    G. Landau and U. Vishkin. Fast string matching with k differences. J. of Computer Systems Science, 37:63–78, 1988.CrossRefMathSciNetzbMATHGoogle Scholar
  9. 9.
    U. Manber and S. Wu. Approximate string matching with arbitrary costs for text and hypertext. In Proc. IAPR Workshop on Structural and Syntactic Pattern Recognition, pages 22–33, Bern, Switzerland, 1992.Google Scholar
  10. 10.
    K. Park and D. Kim. String matching in hypertext. In Proc. CPM'95, pages 318–329, 1995.Google Scholar
  11. 11.
    P. Sellers. The theory and computation of evolutionary distances: pattern recognition. J. of Algorithms, 1:359–373, 1980.zbMATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    E. Sutinen and J. Tarhio. On using q-gram locations in approximate string matching. In Proc. ESA'95, LNCS 979, 1995.Google Scholar
  13. 13.
    Esko Ukkonen. Finding approximate patterns in strings. J. of Algorithms, 6:132–137, 1985.zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    S. Wu and U. Manber. Fast text searching allowing errors. CACM, 35(10):83–91, October 1992.Google Scholar
  15. 15.
    S. Wu, U. Manber, and E. Myers. A sub-quadratic algorithm for approximate limited expression matching. Algorithmica, 15(1):50–67, 1996.MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Gonzalo Navarro
    • 1
  1. 1.Dept. of Computer ScienceUniv. of ChileSantiagoChile

Personalised recommendations