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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2023
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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 |
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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 |
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