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Generalized enhanced suffix array construction in external memory

Overview of attention for article published in Algorithms for Molecular Biology, December 2017
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Title
Generalized enhanced suffix array construction in external memory
Published in
Algorithms for Molecular Biology, December 2017
DOI 10.1186/s13015-017-0117-9
Pubmed ID
Authors

Felipe A. Louza, Guilherme P. Telles, Steve Hoffmann, Cristina D. A. Ciferri

Abstract

Suffix arrays, augmented by additional data structures, allow solving efficiently many string processing problems. The external memory construction of the generalized suffix array for a string collection is a fundamental task when the size of the input collection or the data structure exceeds the available internal memory. In this article we present and analyze [Formula: see text] [introduced in CPM (External memory generalized suffix and [Formula: see text] arrays construction. In: Proceedings of CPM. pp 201-10, 2013)], the first external memory algorithm to construct generalized suffix arrays augmented with the longest common prefix array for a string collection. Our algorithm relies on a combination of buffers, induced sorting and a heap to avoid direct string comparisons. We performed experiments that covered different aspects of our algorithm, including running time, efficiency, external memory access, internal phases and the influence of different optimization strategies. On real datasets of size up to 24 GB and using 2 GB of internal memory, [Formula: see text] showed a competitive performance when compared to [Formula: see text] and [Formula: see text], which are efficient algorithms for a single string according to the related literature. We also show the effect of disk caching managed by the operating system on our algorithm. The proposed algorithm was validated through performance tests using real datasets from different domains, in various combinations, and showed a competitive performance. Our algorithm can also construct the generalized Burrows-Wheeler transform of a string collection with no additional cost except by the output time.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 31%
Professor 3 23%
Student > Bachelor 3 23%
Unspecified 1 8%
Researcher 1 8%
Other 0 0%
Unknown 1 8%
Readers by discipline Count As %
Computer Science 6 46%
Agricultural and Biological Sciences 2 15%
Unspecified 1 8%
Chemistry 1 8%
Engineering 1 8%
Other 0 0%
Unknown 2 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 December 2017.
All research outputs
#19,017,658
of 23,577,654 outputs
Outputs from Algorithms for Molecular Biology
#189
of 248 outputs
Outputs of similar age
#330,910
of 442,920 outputs
Outputs of similar age from Algorithms for Molecular Biology
#3
of 3 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 248 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 11th percentile – i.e., 11% of its peers scored the same or lower than it.
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