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Fast Algorithms for Computing Jaro Similarity.

Joyanta Basak1, Nidhibahen Shah1, Kenneth Haase2

  • 1School of Computing, University of Connecticut, Storrs, Connecticut, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|March 10, 2026
PubMed
Summary
This summary is machine-generated.

We enhanced Jaro and Jaro-Winkler similarity algorithms for faster string comparison. New methods significantly reduce computational costs, especially for large datasets and repeated comparisons in record linkage.

Keywords:
Jaro similaritylinear time algorithmrecord linkagestring similarity

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Area of Science:

  • Computer Science
  • Data Science
  • Computational Linguistics

Background:

  • Jaro and Jaro-Winkler similarity are key for character-based string comparison.
  • Traditional methods have high computational costs, limiting large-scale applications.
  • Previous work introduced a linear-time Jaro similarity algorithm for long strings.

Purpose of the Study:

  • To introduce enhanced algorithms for Jaro and Jaro-Winkler similarity.
  • To improve runtime efficiency for both short and long strings.
  • To reduce computation time for large-scale, repeated string comparisons.

Main Methods:

  • Development of optimized algorithms for Jaro and Jaro-Winkler similarity.
  • Implementation of techniques for efficient handling of shorter strings.
  • Introduction of methods for drastically reducing computation in self-comparison scenarios.

Main Results:

  • Enhanced algorithms show improved runtime performance compared to traditional methods.
  • The new algorithms are effective for both short and long strings.
  • Significant speedups achieved for large datasets and repeated string comparisons.

Conclusions:

  • The enhanced Jaro and Jaro-Winkler algorithms offer substantial computational improvements.
  • These advancements make the algorithms more suitable for large-scale record linkage and entity resolution.
  • The optimized methods address the limitations of traditional string comparison techniques.