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Entropy-Based Approach in Selection Exact String-Matching Algorithms.

Ivan Markić1, Maja Štula2, Marija Zorić3

  • 1Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, 21000 Split, Croatia.

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Choosing the best string-matching algorithm is complex. This study introduces a new methodology measuring algorithm efficiency by character comparison counts, considering text and pattern properties for domain-specific optimization.

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Area of Science:

  • Computer Science
  • Software Engineering
  • Computational Linguistics

Background:

  • String-matching algorithms are fundamental across scientific disciplines.
  • Evaluating algorithm efficiency is challenging, with standard metrics like execution time being insufficient.
  • Existing methods often overlook the influence of text and pattern characteristics on algorithm performance.

Purpose of the Study:

  • To propose a novel methodology for evaluating string-matching algorithm efficiency.
  • To demonstrate that algorithm efficiency is domain-dependent and influenced by text and pattern properties.
  • To provide a quantitative measure for selecting optimal algorithms for specific applications.

Main Methods:

  • Developed a methodology to assess algorithm efficiency using character comparison count metrics.
  • Introduced a formal, quantitative measure independent of implementation and hardware.
  • Modeled algorithm efficiency based on information entropy of search patterns within a specific domain.

Main Results:

  • Algorithm efficiency is shown to depend on the properties of both the search pattern and the text.
  • The proposed character comparison count metrics offer a domain-specific ranking of algorithms.
  • Empirical testing validated the soundness and practical implementation of the methodology.

Conclusions:

  • The developed methodology enables researchers to select efficient string-matching algorithms tailored to specific domains.
  • Character comparison count metrics provide a robust and objective measure of algorithmic performance.
  • This approach addresses the limitations of traditional efficiency evaluations by incorporating domain-specific data.