Occurrence and substring heuristics for δ-matching

  • Authors:

  • Maxime Crochemore;Costas S. Iliopoulos;Thierry Lecroq;Yoan J. Pinzon;Wojciech Plandowski;Wojciech Rytter

  • Affiliations:

  • Institut Gaspard Monge, Université Marne-la-Vallée, 77454 Marne-la-Vallée CEDEX 2, France and King's College London, UK;Dept. of Computer Science, King's College London, London WC2R 2LS, U.K., and School of Computing, Curtin University of Technology, GPO Box 1987 U, WA, Australia;LIFAR-ABISS, Faculté des Sciences et Techniques, Université de Rouen, 76821 Mont-Saint-Aignan CEDEX, France;Dept. of Computer Science, King's College London, London WC2R 2LS, U.K.,;Instytut Informatyki, Uniwersytet Warszawski, Banacha 2, 02-097, Warszawa, Poland;Instytut Informatyki, Uniwersytet Warszawski, Banacha, 2, 02-097, Warszawa, Poland, and Dept. of Computer Science, New Jersey Institute of Technology

  • Venue:
  • Fundamenta Informaticae - Special issue on computing patterns in strings
  • Year:
  • 2002

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Abstract

We consider a version of pattern matching useful in processing large musical data: δ-matching, which consists in finding matches which are δ-approximate in the sense of the distance measured as maximum difference between symbols. The alphabet is an interval of integers, and the distance between two symbols a, b is measured as |a - b|. We also consider (δ, γ)-matching, where γ is a bound on the total sum of the differences. We first consider "occurrence heuristics" by adapting exact string matching algorithms to the two notions of approximate string matching. The resulting algorithms are efficient in practice. Then we consider "substring heuristics". We present δ-matching algorithms fast on the average providing that the pattern is "non-flat" and the alphabet interval is large. The pattern is "flat" if its structure does not vary substantially. The algorithms, named δ- BM1, δ-BM2 and δ-BM3 can be thought as members of the generalized Boyer-Moore family of algorithms. The algorithms are fast on average. This is the first paper on the subject, previously only "occurrence heuristics" have been considered Our substring heuristics are much stronger and refer to larger parts of texts (not only to single positions). We use δ-versions of suffix tries and subword graphs. Surprisingly, in the context of δ-matching subword graphs appear to be superior compared with compact suffix trees.