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Bi-Directional r-Indexes

Authors Yuma Arakawa, Gonzalo Navarro, Kunihiko Sadakane

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Author Details

Yuma Arakawa
  • Department of Mathematical Informatics, The University of Tokyo, Japan
Gonzalo Navarro
  • CeBiB and Department of Computer Science, University of Chile, Santiago, Chile
Kunihiko Sadakane
  • Department of Mathematical Informatics, The University of Tokyo, Japan

Funding

  • Navarro, Gonzalo: Funded in part by Basal Funds FB0001 and Fondecyt Grant 1-200038, ANID, Chile.

Cite AsGet BibTex

Yuma Arakawa, Gonzalo Navarro, and Kunihiko Sadakane. Bi-Directional r-Indexes. In 33rd Annual Symposium on Combinatorial Pattern Matching (CPM 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 223, pp. 11:1-11:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/LIPIcs.CPM.2022.11

Abstract

Indexing highly repetitive texts is important in fields such as bioinformatics and versioned repositories. The run-length compression of the Burrows-Wheeler transform (BWT) provides a compressed representation particularly well-suited to text indexing. The r-index is one such index. It enables fast locating of occurrences of a pattern within O(r) words of space, where r is the number of equal-letter runs in the BWT. Its mechanism of locating is to maintain one suffix array sample along the backward-search of the pattern, and to compute all the pattern positions from that sample once the backward-search is complete. In this paper we develop this algorithm further, and propose a new bi-directional text index called the br-index, which supports extending the matched pattern both in forward and backward directions, and locating the occurrences of the pattern at any step of the search, within O(r+r_R) words of space, where r_R is the number of equal-letter runs in the BWT of the reversed text. Our experiments show that the br-index captures the long repetitions of the text, and outperforms the existing indexes in text searching allowing some mismatches except in an internal part.

Subject Classification

ACM Subject Classification
  • Theory of computation → Data compression
Keywords
  • Compressed text indexes
  • Burrows-Wheeler Transform
  • highly repetitive text collections

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Supplementary Materials

References

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