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An Optimal-Time RLBWT Construction in BWT-Runs Bounded Space

Authors Takaaki Nishimoto, Shunsuke Kanda, Yasuo Tabei



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Takaaki Nishimoto
  • RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
Shunsuke Kanda
  • RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
Yasuo Tabei
  • RIKEN Center for Advanced Intelligence Project, Tokyo, Japan

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Takaaki Nishimoto, Shunsuke Kanda, and Yasuo Tabei. An Optimal-Time RLBWT Construction in BWT-Runs Bounded Space. In 49th International Colloquium on Automata, Languages, and Programming (ICALP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 229, pp. 99:1-99:20, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/LIPIcs.ICALP.2022.99

Abstract

The compression of highly repetitive strings (i.e., strings with many repetitions) has been a central research topic in string processing, and quite a few compression methods for these strings have been proposed thus far. Among them, an efficient compression format gathering increasing attention is the run-length Burrows-Wheeler transform (RLBWT), which is a run-length encoded BWT as a reversible permutation of an input string on the lexicographical order of suffixes. State-of-the-art construction algorithms of RLBWT have a serious issue with respect to (i) non-optimal computation time or (ii) a working space that is linearly proportional to the length of an input string. In this paper, we present r-comp, the first optimal-time construction algorithm of RLBWT in BWT-runs bounded space. That is, the computational complexity of r-comp is O(n + r log r) time and O(r log n) bits of working space for the length n of an input string and the number r of equal-letter runs in BWT. The computation time is optimal (i.e., O(n)) for strings with the property r = O(n/log n), which holds for most highly repetitive strings. Experiments using a real-world dataset of highly repetitive strings show the effectiveness of r-comp with respect to computation time and space.

Subject Classification

ACM Subject Classification
  • Theory of computation → Data compression
Keywords
  • lossless data compression
  • Burrows-Wheeler transform
  • highly repetitive text collections

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