Short-term forecasting models for automated data backup system: segmented regression analysis
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Acta scientiarum. Technology (Online) |
Texto Completo: | http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/46073 |
Resumo: | The Information and Communication Technology (ICT) becomes a critical area to business success; organizations need to adopt additional measures to ensure the availability of their services. However, such services are often not planned, analyzed and monitored, which impacts the assurance quality to customers. The backup is the service addressed in this study, with the object of study of the automated data backup systems in operation at the Federal University of Itajuba - Brazil. The main objective of this research was to present a logical sequence of steps to obtain short-term forecast models that estimate the point at which each recording media reaches its storage capacity limit. The input data was collected in the metadata generated by the backup system, with 2 years data window. For the implementation of the models, the simple univariate linear regression technique was employed in conjunction, in some cases, with the simple segmented linear regression. In order to discover the breakpoint, a targeted approach to residual analysis was applied. The results obtained by the iterative implementation of the proposed algorithm showed adherence to the characteristics of the analyzed series, with accuracy measures, regression significance, normality residual through control charts, model adjustment, among others. As a result, an algorithm was developed for integration into automated backup systems using the methodology described in this study. |
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Acta scientiarum. Technology (Online) |
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Short-term forecasting models for automated data backup system: segmented regression analysisdata backup; short term forecast; segmented single linear regression.The Information and Communication Technology (ICT) becomes a critical area to business success; organizations need to adopt additional measures to ensure the availability of their services. However, such services are often not planned, analyzed and monitored, which impacts the assurance quality to customers. The backup is the service addressed in this study, with the object of study of the automated data backup systems in operation at the Federal University of Itajuba - Brazil. The main objective of this research was to present a logical sequence of steps to obtain short-term forecast models that estimate the point at which each recording media reaches its storage capacity limit. The input data was collected in the metadata generated by the backup system, with 2 years data window. For the implementation of the models, the simple univariate linear regression technique was employed in conjunction, in some cases, with the simple segmented linear regression. In order to discover the breakpoint, a targeted approach to residual analysis was applied. The results obtained by the iterative implementation of the proposed algorithm showed adherence to the characteristics of the analyzed series, with accuracy measures, regression significance, normality residual through control charts, model adjustment, among others. As a result, an algorithm was developed for integration into automated backup systems using the methodology described in this study.Universidade Estadual De Maringá2020-02-28info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/4607310.4025/actascitechnol.v42i1.46073Acta Scientiarum. Technology; Vol 42 (2020): Publicação contínua; e46073Acta Scientiarum. Technology; v. 42 (2020): Publicação contínua; e460731806-25631807-8664reponame:Acta scientiarum. Technology (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/46073/751375149594Copyright (c) 2020 Acta Scientiarum. Technologyhttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessPereira, Leandro DuarteBalestrassi, Pedro PauloPaes, Vinicius de CarvalhoPaiva, Anderson Paulo dePeruchi, Rogério SantanaMendes, Ronã Rinston Amauri2020-05-05T15:19:26Zoai:periodicos.uem.br/ojs:article/46073Revistahttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/indexPUBhttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/oai||actatech@uem.br1807-86641806-2563opendoar:2020-05-05T15:19:26Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM)false |
dc.title.none.fl_str_mv |
Short-term forecasting models for automated data backup system: segmented regression analysis |
title |
Short-term forecasting models for automated data backup system: segmented regression analysis |
spellingShingle |
Short-term forecasting models for automated data backup system: segmented regression analysis Pereira, Leandro Duarte data backup; short term forecast; segmented single linear regression. |
title_short |
Short-term forecasting models for automated data backup system: segmented regression analysis |
title_full |
Short-term forecasting models for automated data backup system: segmented regression analysis |
title_fullStr |
Short-term forecasting models for automated data backup system: segmented regression analysis |
title_full_unstemmed |
Short-term forecasting models for automated data backup system: segmented regression analysis |
title_sort |
Short-term forecasting models for automated data backup system: segmented regression analysis |
author |
Pereira, Leandro Duarte |
author_facet |
Pereira, Leandro Duarte Balestrassi, Pedro Paulo Paes, Vinicius de Carvalho Paiva, Anderson Paulo de Peruchi, Rogério Santana Mendes, Ronã Rinston Amauri |
author_role |
author |
author2 |
Balestrassi, Pedro Paulo Paes, Vinicius de Carvalho Paiva, Anderson Paulo de Peruchi, Rogério Santana Mendes, Ronã Rinston Amauri |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Pereira, Leandro Duarte Balestrassi, Pedro Paulo Paes, Vinicius de Carvalho Paiva, Anderson Paulo de Peruchi, Rogério Santana Mendes, Ronã Rinston Amauri |
dc.subject.por.fl_str_mv |
data backup; short term forecast; segmented single linear regression. |
topic |
data backup; short term forecast; segmented single linear regression. |
description |
The Information and Communication Technology (ICT) becomes a critical area to business success; organizations need to adopt additional measures to ensure the availability of their services. However, such services are often not planned, analyzed and monitored, which impacts the assurance quality to customers. The backup is the service addressed in this study, with the object of study of the automated data backup systems in operation at the Federal University of Itajuba - Brazil. The main objective of this research was to present a logical sequence of steps to obtain short-term forecast models that estimate the point at which each recording media reaches its storage capacity limit. The input data was collected in the metadata generated by the backup system, with 2 years data window. For the implementation of the models, the simple univariate linear regression technique was employed in conjunction, in some cases, with the simple segmented linear regression. In order to discover the breakpoint, a targeted approach to residual analysis was applied. The results obtained by the iterative implementation of the proposed algorithm showed adherence to the characteristics of the analyzed series, with accuracy measures, regression significance, normality residual through control charts, model adjustment, among others. As a result, an algorithm was developed for integration into automated backup systems using the methodology described in this study. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-02-28 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/46073 10.4025/actascitechnol.v42i1.46073 |
url |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/46073 |
identifier_str_mv |
10.4025/actascitechnol.v42i1.46073 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/46073/751375149594 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2020 Acta Scientiarum. Technology https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2020 Acta Scientiarum. Technology https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Estadual De Maringá |
publisher.none.fl_str_mv |
Universidade Estadual De Maringá |
dc.source.none.fl_str_mv |
Acta Scientiarum. Technology; Vol 42 (2020): Publicação contínua; e46073 Acta Scientiarum. Technology; v. 42 (2020): Publicação contínua; e46073 1806-2563 1807-8664 reponame:Acta scientiarum. Technology (Online) instname:Universidade Estadual de Maringá (UEM) instacron:UEM |
instname_str |
Universidade Estadual de Maringá (UEM) |
instacron_str |
UEM |
institution |
UEM |
reponame_str |
Acta scientiarum. Technology (Online) |
collection |
Acta scientiarum. Technology (Online) |
repository.name.fl_str_mv |
Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM) |
repository.mail.fl_str_mv |
||actatech@uem.br |
_version_ |
1799315336969846784 |