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Fixed Block Compression Boosting in FM-Indexes: Theory and Practice
Author(s) -
Simon Gog,
Juha Kärkkäinen,
Dominik Kempa,
Matthias Petri,
Simon J. Puglisi
Publication year - 2018
Publication title -
algorithmica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.647
H-Index - 78
eISSN - 1432-0541
pISSN - 0178-4617
DOI - 10.1007/s00453-018-0475-9
Subject(s) - boosting (machine learning) , computer science , theory of computation , data compression , algorithm , block (permutation group theory) , data structure , theoretical computer science , mathematics , artificial intelligence , geometry , programming language
The FM index (Ferragina and Manzini in J ACM 52(4):552–581, 2005) is a widely-used compressed data structure that stores a string T in a compressed form and also supports fast pattern matching queries. In this paper, we describe new FM-index variants that combine nice theoretical properties, simple implementation and improved practical performance. Our main theoretical result is a new technique called fixed block compression boosting, which is a simpler and faster alternative to optimal compression boosting and implicit compression boosting used in previous FM-indexes. We also describe several new techniques for implementing fixed-block boosting efficiently, including a new, fast, and space-efficient implementation of wavelet trees. Our extensive experiments show the new indexes to be consistently fast and small relative to the state-of-the-art, and thus they make a good “off-the-shelf” choice for many applications.

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