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On Compressing Collections of Substring Samples.

Authors: Golnaz Badkobeh; Sara Giuliani; Zsuzsanna Liptak; Simon J. Puglisi:;

On Compressing Collections of Substring Samples.

Abstract

Given a string X = X[1..n] of length n, and integers m and s, such that n > m ≥ 2s > 0, we consider the problem of compressing the string S formed by concatenating the substrings of X of length m starting at positions i ≡ 1 (mod s). In particular, we provide an upper bound of (2n − m)/s + 2z + (m − s) on the size of the Lempel-Ziv (LZ77) parsing of S, where z is the size of the parsing of X. We also show that a related bound holds regardless of the order in which the substrings are concatenated in the formation of S. If X is viewed as a genome sequence, the above substring sampling process corresponds to an idealized model of short read DNA sequencing.

Peer reviewed

Countries
Italy, Finland
Keywords

strings, Lempel-Ziv, LZ77, Computer and information sciences, parsing, repetitiveness, combinatorics on words, Lempel-Ziv, LZ77, data compression, strings, repetitiveness, combinatorics on words, parsing, short reads, data compression, short reads

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selected citations
These citations are derived from selected sources.
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!
0
Average
Average
Average
Green