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Streaming k-Edit Approximate Pattern Matching via String Decomposition

Authors Sudatta Bhattacharya , Michal Koucký

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ACM Subject Classification
  • Theory of computation → Pattern matching
  • Theory of computation → Sketching and sampling
Keywords
  • Approximate pattern matching
  • edit distance
  • streaming algorithms

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Abstract

In this paper we give an algorithm for streaming k-edit approximate pattern matching which uses space Õ(k²) and time Õ(k²) per arriving symbol. This improves substantially on the recent algorithm of Kociumaka, Porat and Starikovskaya [Kociumaka et al., 2022] which uses space Õ(k⁵) and time Õ(k⁸) per arriving symbol. In the k-edit approximate pattern matching problem we get a pattern P and text T and we want to identify all substrings of the text T that are at edit distance at most k from P. In the streaming version of this problem both the pattern and the text arrive in a streaming fashion symbol by symbol and after each symbol of the text we need to report whether there is a current suffix of the text with edit distance at most k from P. We measure the total space needed by the algorithm and time needed per arriving symbol.

Cite As Get BibTex

Sudatta Bhattacharya and Michal Koucký. Streaming k-Edit Approximate Pattern Matching via String Decomposition. In 50th International Colloquium on Automata, Languages, and Programming (ICALP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 261, pp. 22:1-22:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.ICALP.2023.22

Author Details

Sudatta Bhattacharya
  • Computer Science Institute of Charles University, Prague, Czech Republic
Michal Koucký
  • Computer Science Institute of Charles University, Prague, Czech Republic

Funding

  • Bhattacharya, Sudatta: Partially supported by the Grant Agency of the Czech Republic under the grant agreement no. 19-27871X.
  • Koucký, Michal: Partially supported by the Grant Agency of the Czech Republic under the grant agreement no. 19-27871X.

Acknowledgements

We thank Tomasz Kociumaka for pointing to us references for Corollary 3. We thank anonymous reviewers for helpful comments.

References

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