An efficient parallel optimization for co-authorship network analysis
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/PDCAT.2017.00030 http://hdl.handle.net/11449/176308 |
Resumo: | Co-authorship analysis in science and technology partnerships provides a vision of cooperation patterns between individuals and organizations and is still widely used to understand and assess scientific collaboration patterns. This analysis is conducted by means of bibliometry, which is the quantitative study of scientific production. However, with the evolution of database management systems, there was a significant increase in the volume of stored data, which could difficult the analysis. In this context, the developed work presents an efficient parallel optimization of bibliometric information, in order to allow this scientific analysis in a Big Data environment. Our results show that the time taken to calculate the transitivity value using the sequential approach grows 4.08 times faster than the parallel proposed approach when the number of nodes tends to infinity; the time taken to calculate the average distance and diameter values using the sequential approach grows 5.27 times faster than the parallel proposed approach when the number of nodes tends to infinity. Also, the results found present good values of speed up and efficiency. |
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Repositório Institucional da UNESP |
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An efficient parallel optimization for co-authorship network analysisBibliometricsBig dataCoauthorship networkGraphsKnowledge extractionNoSQLCo-authorship analysis in science and technology partnerships provides a vision of cooperation patterns between individuals and organizations and is still widely used to understand and assess scientific collaboration patterns. This analysis is conducted by means of bibliometry, which is the quantitative study of scientific production. However, with the evolution of database management systems, there was a significant increase in the volume of stored data, which could difficult the analysis. In this context, the developed work presents an efficient parallel optimization of bibliometric information, in order to allow this scientific analysis in a Big Data environment. Our results show that the time taken to calculate the transitivity value using the sequential approach grows 4.08 times faster than the parallel proposed approach when the number of nodes tends to infinity; the time taken to calculate the average distance and diameter values using the sequential approach grows 5.27 times faster than the parallel proposed approach when the number of nodes tends to infinity. Also, the results found present good values of speed up and efficiency.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Department of Computer Science and Statistics São Paulo State University (Unesp) Institute of Biosciences Humanities and Exact Sciences (Ibilce) Campus São José Do Rio PretoDepartment of Computer Science Federal University of São Carlos (UFSCAR)Department of Computer Science and Statistics São Paulo State University (Unesp) Institute of Biosciences Humanities and Exact Sciences (Ibilce) Campus São José Do Rio PretoUniversidade Estadual Paulista (Unesp)Universidade Federal de São Carlos (UFSCar)Valencio, Carlos Roberto [UNESP]De Freitas, Jose Carlos [UNESP]Gratao De Souza, Rogeria Cristiane [UNESP]Neves, Leandro Alves [UNESP]Donega Zafalon, Geraldo Francisco [UNESP]Colombini, Angelo CesarTenorio, William [UNESP]2018-12-11T17:20:03Z2018-12-11T17:20:03Z2018-03-27info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject127-134http://dx.doi.org/10.1109/PDCAT.2017.00030Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings, v. 2017-December, p. 127-134.http://hdl.handle.net/11449/17630810.1109/PDCAT.2017.000302-s2.0-85046774100464481225387583221390538148793120000-0002-9325-3159Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengParallel and Distributed Computing, Applications and Technologies, PDCAT Proceedingsinfo:eu-repo/semantics/openAccess2021-10-23T21:47:03Zoai:repositorio.unesp.br:11449/176308Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T14:34:55.497496Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
An efficient parallel optimization for co-authorship network analysis |
title |
An efficient parallel optimization for co-authorship network analysis |
spellingShingle |
An efficient parallel optimization for co-authorship network analysis Valencio, Carlos Roberto [UNESP] Bibliometrics Big data Coauthorship network Graphs Knowledge extraction NoSQL |
title_short |
An efficient parallel optimization for co-authorship network analysis |
title_full |
An efficient parallel optimization for co-authorship network analysis |
title_fullStr |
An efficient parallel optimization for co-authorship network analysis |
title_full_unstemmed |
An efficient parallel optimization for co-authorship network analysis |
title_sort |
An efficient parallel optimization for co-authorship network analysis |
author |
Valencio, Carlos Roberto [UNESP] |
author_facet |
Valencio, Carlos Roberto [UNESP] De Freitas, Jose Carlos [UNESP] Gratao De Souza, Rogeria Cristiane [UNESP] Neves, Leandro Alves [UNESP] Donega Zafalon, Geraldo Francisco [UNESP] Colombini, Angelo Cesar Tenorio, William [UNESP] |
author_role |
author |
author2 |
De Freitas, Jose Carlos [UNESP] Gratao De Souza, Rogeria Cristiane [UNESP] Neves, Leandro Alves [UNESP] Donega Zafalon, Geraldo Francisco [UNESP] Colombini, Angelo Cesar Tenorio, William [UNESP] |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Universidade Federal de São Carlos (UFSCar) |
dc.contributor.author.fl_str_mv |
Valencio, Carlos Roberto [UNESP] De Freitas, Jose Carlos [UNESP] Gratao De Souza, Rogeria Cristiane [UNESP] Neves, Leandro Alves [UNESP] Donega Zafalon, Geraldo Francisco [UNESP] Colombini, Angelo Cesar Tenorio, William [UNESP] |
dc.subject.por.fl_str_mv |
Bibliometrics Big data Coauthorship network Graphs Knowledge extraction NoSQL |
topic |
Bibliometrics Big data Coauthorship network Graphs Knowledge extraction NoSQL |
description |
Co-authorship analysis in science and technology partnerships provides a vision of cooperation patterns between individuals and organizations and is still widely used to understand and assess scientific collaboration patterns. This analysis is conducted by means of bibliometry, which is the quantitative study of scientific production. However, with the evolution of database management systems, there was a significant increase in the volume of stored data, which could difficult the analysis. In this context, the developed work presents an efficient parallel optimization of bibliometric information, in order to allow this scientific analysis in a Big Data environment. Our results show that the time taken to calculate the transitivity value using the sequential approach grows 4.08 times faster than the parallel proposed approach when the number of nodes tends to infinity; the time taken to calculate the average distance and diameter values using the sequential approach grows 5.27 times faster than the parallel proposed approach when the number of nodes tends to infinity. Also, the results found present good values of speed up and efficiency. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-12-11T17:20:03Z 2018-12-11T17:20:03Z 2018-03-27 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/PDCAT.2017.00030 Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings, v. 2017-December, p. 127-134. http://hdl.handle.net/11449/176308 10.1109/PDCAT.2017.00030 2-s2.0-85046774100 4644812253875832 2139053814879312 0000-0002-9325-3159 |
url |
http://dx.doi.org/10.1109/PDCAT.2017.00030 http://hdl.handle.net/11449/176308 |
identifier_str_mv |
Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings, v. 2017-December, p. 127-134. 10.1109/PDCAT.2017.00030 2-s2.0-85046774100 4644812253875832 2139053814879312 0000-0002-9325-3159 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
127-134 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128383065784320 |