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https://dx.doi.org/10.48550/ar...
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Suffix sorting via matching statistics

Authors: Zsuzsanna Lipták; Francesco Masillo; Simon J. Puglisi;

Suffix sorting via matching statistics

Abstract

AbstractWe introduce a new algorithm for constructing the generalized suffix array of a collection of highly similar strings. As a first step, we construct a compressed representation of the matching statistics of the collection with respect to a reference string. We then use this data structure to distribute suffixes into a partial order, and subsequently to speed up suffix comparisons to complete the generalized suffix array. Our experimental evidence with a prototype implementation (a tool we call ) shows that on string collections with highly similar strings we can construct the suffix array in time competitive with or faster than the fastest available methods. Along the way, we describe a heuristic for fast computation of the matching statistics of two strings, which may be of independent interest.

Keywords

FOS: Computer and information sciences, Data structures, String collections, QH301-705.5, Matching statistics, QH426-470, compressed representation, data structures, Computer Science - Data Structures and Algorithms, Generalized suffix array, Genetics, efficient algorithms, Data Structures and Algorithms (cs.DS), matching statistics, Biology (General), Computer and information sciences, Research, string collections, Compressed representation, 004, Generalized suffix array; Matching statistics; String collections; Compressed representation; Data structures; efficient algorithms, Efficient algorithms, Generalized suffix array, matching statistics, string collections, compressed representation, data structures, efficient algorithms, ddc: ddc:004

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
Green
gold