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.
@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} }
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