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Generalized Pattern Matching Statistics

  • Jérémie Bourdon
  • Brigitte Vallée
Conference paper
Part of the Trends in Mathematics book series (TM)

Abstract

In pattern matching algorithms, a characteristic parameter is the number of occurrences of a given pattern in a random text of length n generated by a source. We consider here a generalization of the pattern matching problem in two ways. First, we deal with a generalized notion of pattern that encompasses classical patterns as well as “hidden patterns”. Second, we consider a quite general probabilistic model of sources that may possess a high degree of correlations. Such sources are built with dynamical systems and are called dynamical sources. We determine the mean and the variance of the number of occurrences in this generalized pattern matching problem, and establish a property of concentration of distribution. These results are obtained via combinatorics, formal language techniques, and methods of analytic combinatorics based on generating operators and generating functions. The generating operators come from the dynamical system framework and generate themselves generating functions. The motivation to study this problem comes from an attempt at finding a reliable threshold for intrusion detections, from textual data processing applications, and from molecular biology.

Keywords

Generate Operator Intrusion Detection Pattern Match Regular Language Dynamical Source 
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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Copyright information

© Springer Basel AG 2002

Authors and Affiliations

  • Jérémie Bourdon
    • 1
  • Brigitte Vallée
    • 1
  1. 1.GREYC—UMR 6072Université de CaenCaenFrance

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