Cargando…

Time series big data: a survey on data stream frameworks, analysis and algorithms

Big data has a substantial role nowadays, and its importance has significantly increased over the last decade. Big data’s biggest advantages are providing knowledge, supporting the decision-making process, and improving the use of resources, services, and infrastructures. The potential of big data i...

Descripción completa

Detalles Bibliográficos
Autores principales: Almeida, Ana, Brás, Susana, Sargento, Susana, Pinto, Filipe Cabral
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10225118/
https://www.ncbi.nlm.nih.gov/pubmed/37274443
http://dx.doi.org/10.1186/s40537-023-00760-1
_version_ 1785050330738720768
author Almeida, Ana
Brás, Susana
Sargento, Susana
Pinto, Filipe Cabral
author_facet Almeida, Ana
Brás, Susana
Sargento, Susana
Pinto, Filipe Cabral
author_sort Almeida, Ana
collection PubMed
description Big data has a substantial role nowadays, and its importance has significantly increased over the last decade. Big data’s biggest advantages are providing knowledge, supporting the decision-making process, and improving the use of resources, services, and infrastructures. The potential of big data increases when we apply it in real-time by providing real-time analysis, predictions, and forecasts, among many other applications. Our goal with this article is to provide a viewpoint on how to build a system capable of processing big data in real-time, performing analysis, and applying algorithms. A system should be designed to handle vast amounts of data and provide valuable knowledge through analysis and algorithms. This article explores the current approaches and how they can be used for the real-time operations and predictions.
format Online
Article
Text
id pubmed-10225118
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Springer International Publishing
record_format MEDLINE/PubMed
spelling pubmed-102251182023-05-30 Time series big data: a survey on data stream frameworks, analysis and algorithms Almeida, Ana Brás, Susana Sargento, Susana Pinto, Filipe Cabral J Big Data Survey Big data has a substantial role nowadays, and its importance has significantly increased over the last decade. Big data’s biggest advantages are providing knowledge, supporting the decision-making process, and improving the use of resources, services, and infrastructures. The potential of big data increases when we apply it in real-time by providing real-time analysis, predictions, and forecasts, among many other applications. Our goal with this article is to provide a viewpoint on how to build a system capable of processing big data in real-time, performing analysis, and applying algorithms. A system should be designed to handle vast amounts of data and provide valuable knowledge through analysis and algorithms. This article explores the current approaches and how they can be used for the real-time operations and predictions. Springer International Publishing 2023-05-28 2023 /pmc/articles/PMC10225118/ /pubmed/37274443 http://dx.doi.org/10.1186/s40537-023-00760-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Survey
Almeida, Ana
Brás, Susana
Sargento, Susana
Pinto, Filipe Cabral
Time series big data: a survey on data stream frameworks, analysis and algorithms
title Time series big data: a survey on data stream frameworks, analysis and algorithms
title_full Time series big data: a survey on data stream frameworks, analysis and algorithms
title_fullStr Time series big data: a survey on data stream frameworks, analysis and algorithms
title_full_unstemmed Time series big data: a survey on data stream frameworks, analysis and algorithms
title_short Time series big data: a survey on data stream frameworks, analysis and algorithms
title_sort time series big data: a survey on data stream frameworks, analysis and algorithms
topic Survey
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10225118/
https://www.ncbi.nlm.nih.gov/pubmed/37274443
http://dx.doi.org/10.1186/s40537-023-00760-1
work_keys_str_mv AT almeidaana timeseriesbigdataasurveyondatastreamframeworksanalysisandalgorithms
AT brassusana timeseriesbigdataasurveyondatastreamframeworksanalysisandalgorithms
AT sargentosusana timeseriesbigdataasurveyondatastreamframeworksanalysisandalgorithms
AT pintofilipecabral timeseriesbigdataasurveyondatastreamframeworksanalysisandalgorithms