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Many Flavors of Edit Distance

Authors Sudatta Bhattacharya , Sanjana Dey , Elazar Goldenberg , Michal Koucký

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Author Details

Sudatta Bhattacharya
  • Charles University, Prague, Czech Republic
Sanjana Dey
  • National University of Singapore, Singapore
Elazar Goldenberg
  • The Academic College of Tel-Aviv-Yaffo, Israel
Michal Koucký
  • Charles University, Prague, Czech Republic

Funding

  • Bhattacharya, Sudatta: Partially supported by the project of Czech Science Foundation no. 19-27871X, 24-10306S and by the project GAUK125424 of the Charles University Grant Agency.
  • Koucký, Michal: Partially supported by the project of Czech Science Foundation no. 19-27871X and 24-10306S.

Acknowledgements

The project started at EPAC Workshop: Algorithms and Complexity partially supported by the project of Czech Science Foundation no. 19-27871X.

Cite As Get BibTex

Sudatta Bhattacharya, Sanjana Dey, Elazar Goldenberg, and Michal Koucký. Many Flavors of Edit Distance. In 44th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 323, pp. 11:1-11:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/LIPIcs.FSTTCS.2024.11

Abstract

Several measures exist for string similarity, including notable ones like the edit distance and the indel distance. The former measures the count of insertions, deletions, and substitutions required to transform one string into another, while the latter specifically quantifies the number of insertions and deletions. Many algorithmic solutions explicitly address one of these measures, and frequently techniques applicable to one can also be adapted to work with the other. In this paper, we investigate whether there exists a standardized approach for applying results from one setting to another. Specifically, we demonstrate the capability to reduce questions regarding string similarity over arbitrary alphabets to equivalent questions over a binary alphabet. Furthermore, we illustrate how to transform questions concerning indel distance into equivalent questions based on edit distance. This complements an earlier result of Tiskin (2007) which addresses the inverse direction.

Subject Classification

ACM Subject Classification
  • Theory of computation → Random projections and metric embeddings
Keywords
  • Edit distance
  • Indel distance
  • Embedding
  • LCS
  • Alphabet Reduction

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References

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