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Move-r: Optimizing the r-index

Authors: Nico Bertram, Johannes Fischer, and Lukas Nalbach

Published in: LIPIcs, Volume 301, 22nd International Symposium on Experimental Algorithms (SEA 2024)


Abstract
We present a static text index called Move-r, which is a highly optimized version of the r-index ([Travis Gagie et al., 2020] Gagie et al., 2020) that encorporates recent theoretical developments of the move data structure ([Takaaki Nishimoto and Yasuo Tabei, 2021] Nishimoto and Tabei, 2021). The r-index is the method of choice for indexing highly repetitive texts, such as different versions of a text document or DNA from the same species, as it exploits the compressibilty of the underlying data. With Move-r, we can answer count- and locate queries 2-35 (typically 15) times as fast as with any other r-index supporting locate queries while being 0.8-2.5 (typically 2) times as large. A Move-r index can be constructed 0.9-2 (typically 2) times as fast while using 1/3-1 (typically 1/2) times as much space.

Cite as

Nico Bertram, Johannes Fischer, and Lukas Nalbach. Move-r: Optimizing the r-index. In 22nd International Symposium on Experimental Algorithms (SEA 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 301, pp. 1:1-1:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)


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@InProceedings{bertram_et_al:LIPIcs.SEA.2024.1,
  author =	{Bertram, Nico and Fischer, Johannes and Nalbach, Lukas},
  title =	{{Move-r: Optimizing the r-index}},
  booktitle =	{22nd International Symposium on Experimental Algorithms (SEA 2024)},
  pages =	{1:1--1:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-325-6},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{301},
  editor =	{Liberti, Leo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2024.1},
  URN =		{urn:nbn:de:0030-drops-203662},
  doi =		{10.4230/LIPIcs.SEA.2024.1},
  annote =	{Keywords: Compressed Text Index, Burrows-Wheeler Transform}
}