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Computing MEMs on Repetitive Text Collections

Author Gonzalo Navarro

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Author Details

Gonzalo Navarro
  • Center for Biotechnology and Bioengineering (CeBiB), Santiago, Chile
  • Department of Computer Science, University of Chile, Santiago, Chile

Funding

Funded by Basal Funds FB0001, Mideplan, Chile, and Fondecyt Grant 1-200038, Chile.

Cite AsGet BibTex

Gonzalo Navarro. Computing MEMs on Repetitive Text Collections. In 34th Annual Symposium on Combinatorial Pattern Matching (CPM 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 259, pp. 24:1-24:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.CPM.2023.24

Abstract

We consider the problem of computing the Maximal Exact Matches (MEMs) of a given pattern P[1..m] on a large repetitive text collection T[1..n], which is represented as a (hopefully much smaller) run-length context-free grammar of size g_{rl}. We show that the problem can be solved in time O(m² log^ε n), for any constant ε > 0, on a data structure of size O(g_{rl}). Further, on a locally consistent grammar of size O(δ log n/δ), the time decreases to O(m log m(log m + log^ε n)). The value δ is a function of the substring complexity of T and Ω(δ log n/δ) is a tight lower bound on the compressibility of repetitive texts T, so our structure has optimal size in terms of n and δ.

Subject Classification

ACM Subject Classification
  • Theory of computation → Data structures design and analysis
Keywords
  • grammar-based indices
  • maximal exact matches
  • locally consistent grammars
  • substring complexity

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