Compressed full-text indexes

Author:

Navarro Gonzalo1,Mäkinen Veli2

Affiliation:

1. University of Chile, Santiago, Chile

2. University of Helsinki, Helsinki, Finland

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.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

Cited by 499 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Accelerating range minimum queries with ray tracing cores;Future Generation Computer Systems;2024-08

2. Whole-Genome Alignment: Methods, Challenges, and Future Directions;Applied Sciences;2024-06-03

3. r-indexing the eBWT;Information and Computation;2024-06

4. Constructing and indexing the bijective and extended Burrows–Wheeler transform;Information and Computation;2024-03

5. CoCo-trie: Data-aware compression and indexing of strings;Information Systems;2024-02

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3