Compressed full-text indexes
Abstract
Full-text indexes provide fast substring search over large text collections. A serious problem of these indexes has traditionally been their space consumption. A recent trend is to develop indexes that exploit the compressibility of the text, so that their size is a function of the compressed text length. This concept has evolved into self-indexes , which in addition contain enough information to reproduce any text portion, so they replace the text. The exciting possibility of an index that takes space close to that of the compressed text, replaces it, and in addition provides fast search over it, has triggered a wealth of activity and produced surprising results in a very short time, which radically changed the status of this area in less than 5 years. The most successful indexes nowadays are able to obtain almost optimal space and search time simultaneously. In this article we present the main concepts underlying (compressed) self-indexes. We explain the relationship between text entropy and regularities that show up in index structures and permit compressing them. Then we cover the most relevant self-indexes, focusing on how they exploit text compressibility to achieve compact structures that can efficiently solve various search problems. Our aim is to give the background to understand and follow the developments in this area.
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The Lwords algorithm can perform its sequence comparison alignment-free approach for 29 primates' genomes, ranging from 14 to 25 Mbp, in just 66 seconds.
“It can perform its sequence comparison alignment-free approach for 29 primates' genomes (ranging from 14 to 25 Mbp) in just 66 s.”
From Whole-Genome Alignment: Methods, Challenges, and Future Directions (2024) · cites this paper as [22]
The Lwords algorithm performs well when there are significant repetitions in the input data, but it can be ineffective for data with minimal redundancy.
“It performs well when there are significant repetitions in the input data; however, it can be ineffective for data with minimal redundancy [22].”
From Whole-Genome Alignment: Methods, Challenges, and Future Directions (2024) · cites this paper as [22]
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Whole-Genome Alignment: Methods, Challenges, and Future Directions
Saada, B., Zhang, T., Siga, E., Zhang, J., & Magalhães Muniz, M. M. (2024) · Applied Sciences · doi:10.3390/app14114837 · Cited by 15 · see citing works
Efficient inverted index with n-gram sampling for string matching in Arabic documents
Nagoudi, E. M. B., Khorsi, A., & Cherroun, H. (2016) · 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa.2016.7945743 · Cited by 1 · see citing works
- Authors
- Navarro, G., & Mäkinen, V.
- Affiliations
- University of Chile (CL)
- University of Helsinki (FI)
- Venue
- ACM Computing Surveys
- Published
- 2007-04-12
- Volume
- 39
- Issue
- 1
- Pages
- 2
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2088386938
- Cited by
- 735
- References
- 0
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 8 · 2023: 9 · 2022: 10 · 2021: 21 · 2020: 20 · 2019: 23 · 2018: 30 · 2017: 56 · 2016: 41 · 2015: 40 · 2014: 62 · 2013: 65 · 2012: 61
- FWCI
- 72.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Computing Surveys (publishedVersion)
- Universidad de Chile (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Algorithms and Data Compression
- Physical Sciences › Computer Science › Computer Networks and Communications › Advanced Database Systems and Queries
- Physical Sciences › Computer Science › Computational Theory and Mathematics › semigroups and automata theory
- Keywords
- text compression, substring search, space-efficient indexing, search time
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