Skip to main content
SpringerLink
Log in

Super-pattern matching

  • Published:
Algorithmica Aims and scope Submit manuscript

Abstract

Some recognition problems are either too complex or too ambiguous to be expressed as a simple pattern matching problem using a sequence or regular expression pattern. In these cases, a richer environment is needed to describe the “patterns” and recognition techniques used to perform the recognition. Some researchers have turned to artificial-intelligence techniques and multistep matching approaches for the problems of gene recognition [5], [7], [18], protein structure recognition [13], and on-line character recognition [6]. This paper presents a class of problems which involve finding matches to “patterns of patterns,” orsuper- patterns, given solutions to the lower-level patterns. The expressiveness of this problem class rivals that of traditional artificial-intelligence characterizations, and yet polynomial-time algorithms are described for each problem in the class.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Allen, F. E. Control Flow Analysis.SIGPLAN Notices,5 (1970), 1–19.

    Article  Google Scholar 

  2. Arbarbanel, R. M., P. R. Wieneke, E. Mansfield, D. A. Jaffe, and D. L. Brutlag. Rapid Searches for Complex Patterns in Biological Molecules.Nucleic Acids Res.,12(1) (1984), 263–280.

    Article  Google Scholar 

  3. Brzozowski, J. A. Derivatives of Regular Expressions.J. Assoc. Comput. Mach.,11 (1964), 481–494.

    MATH  MathSciNet  Google Scholar 

  4. Barley, J. An Efficient Context-Free Parsing Algorithm.Comm. ACM,13(2) (1970), 94–102.

    Article  Google Scholar 

  5. Fields, C. A., and C. A. Soderlund. gm: A Practical Tool for Automating DNA Sequence Analysis.CABIOS,6 (1990), 263–270.

    Google Scholar 

  6. Fujisaki, T., T. E. Chefalas, J. Kim, C. C. Tappert, and C. G. Wolf. Online Run-On Character Recognition: Design and Performance.Internat. J. Pattern Recognition Artificial Intelligence,5 (1991), 123–137.

    Article  Google Scholar 

  7. Guigó, R., S. Knudsen, N. Drake, and T. Smith. Prediction of Gene Structure.J. Mol. Biol.,226 (1992), 141–157.

    Article  Google Scholar 

  8. Hecht, M. S., and J. D. Ullman. A Simple Algorithm for Global Dataflow Analysis Problems.SIAM J. Comput.,4(4) (1975), 519–532.

    Article  MATH  MathSciNet  Google Scholar 

  9. Hirst, S. C. A New Algorithm Solving Membership of Extended Regular Expressions. Draft.

  10. Hopcroft, J. E., and J. D. Ullman.Introduction to Automata Theory, Languages, and Computation. Addison-Wesley, Reading, MA, 1979, Chapter 2.

    MATH  Google Scholar 

  11. Kasami, T. An Efficient Recognition and Syntax-Analysis Algorithm for Context-Free Languages. AFCRL-65-758, Air Force Cambridge Research Laboratory, Bedford, MA (1965).

    Google Scholar 

  12. Lapedes, A., C. Barnes, C. Burks, R. Farber, and K. Sirotkin. Application of Neural Networks and Other Machine Learning Algorithms to DNA Sequence Analysis. InComputers and DNA, SFI Studies in the Sciences of Complexity, vol. VII (Eds. G. Bell and T. Marr). Addison-Wesley, Redwood City, CA, 1989.

    Google Scholar 

  13. Lathrop, R. H., T. A. Webster, and T. F. Smith. Ariadne: Pattern-Directed Inference and Hierarchical Abstraction in Protein Structure Recognition.Comm. ACM,30(11) (1987), 909–921.

    Article  MATH  Google Scholar 

  14. Lectures and Discussions. Workshop on Recognizing Genes. Aspen Center for Physics, May–June, 1990.

  15. Myers, E. W., and W. Miller. Approximate Matching of Regular Expressions.Bull. Math. Biol.,51(1) (1989), 5–37.

    MATH  MathSciNet  Google Scholar 

  16. Needleman, S. B., and C. D. Wunsch. A General Method Applicable to the Search for Similarities in the Amino Acid Sequence of Two Proteins.J. Mol. Biol.,48 (1970), 443–453.

    Article  Google Scholar 

  17. Sankoff, D. Matching Sequences Under Deletion/Insertion Constraints.Proc. Nat. Acad. Sci. USA,69 (1972), 4–6.

    Article  MATH  MathSciNet  Google Scholar 

  18. Searls, D. Investigating the Linguistics of DNA with Definite Clause Grammars.Proc. North American Conf. on Logic Programming, vol. 1, 1989, pp. 189–208.

    Google Scholar 

  19. Stormo, G. Computer Methods for Analyzing Sequence Recognition of Nucleic Acids.Rev. Biophys. Chem.,17 (1988), 241–263.

    Article  Google Scholar 

  20. Wagner, R. A., and M. J. Fischer. The String-to-String Correction Problem.J. Assoc. Comput. Mach.,21(1) (1974), 168–173.

    MATH  MathSciNet  Google Scholar 

  21. Wagner, R. A., and J. I. Seiferas. Correcting Counter-Automaton-Recognizable Languages.SIAM J. Comput.,7(3) (1978), 357–375.

    Article  MATH  MathSciNet  Google Scholar 

  22. Younger, D. H. Recognition and Parsing of Context-Free Languages in Timen 3.Inform, and Control,10(2) (1967), 189–208.

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Arizona, 85719, Tucson, AZ, USA

    J. R. Knight & E. W. Myers

Authors
  1. J. R. Knight
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E. W. Myers
    View author publications

    You can also search for this author in PubMed Google Scholar

Additional information

Communicated by E. W. Myers.

This work was supported in part by the National Institute of Health under Grant ROI LM04960 and by the Aspen Center for Physics.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Knight, J.R., Myers, E.W. Super-pattern matching. Algorithmica 13, 211–243 (1995). https://doi.org/10.1007/BF01188587

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01188587

Key words

Search

Navigation