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A stream processing abstraction framework
Real-time analysis of large multimedia streams is nowadays made efficient by the existence of several Big Data streaming platforms, like Apache Flink and Samza. However, the use of such platforms is difficult due to the fact that facilities they offer are often too raw to be effectively exploited by...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10634501/ https://www.ncbi.nlm.nih.gov/pubmed/37953916 http://dx.doi.org/10.3389/fdata.2023.1227156 |
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author | Bartolini, Ilaria Patella, Marco |
author_facet | Bartolini, Ilaria Patella, Marco |
author_sort | Bartolini, Ilaria |
collection | PubMed |
description | Real-time analysis of large multimedia streams is nowadays made efficient by the existence of several Big Data streaming platforms, like Apache Flink and Samza. However, the use of such platforms is difficult due to the fact that facilities they offer are often too raw to be effectively exploited by analysts. We describe the evolution of RAM3S, a software infrastructure for the integration of Big Data stream processing platforms, to SPAF, an abstraction framework able to provide programmers with a simple but powerful API to ease the development of stream processing applications. By using SPAF, the programmer can easily implement real-time complex analyses of massive streams on top of a distributed computing infrastructure, able to manage the volume and velocity of Big Data streams, thus effectively transforming data into value. |
format | Online Article Text |
id | pubmed-10634501 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-106345012023-11-10 A stream processing abstraction framework Bartolini, Ilaria Patella, Marco Front Big Data Big Data Real-time analysis of large multimedia streams is nowadays made efficient by the existence of several Big Data streaming platforms, like Apache Flink and Samza. However, the use of such platforms is difficult due to the fact that facilities they offer are often too raw to be effectively exploited by analysts. We describe the evolution of RAM3S, a software infrastructure for the integration of Big Data stream processing platforms, to SPAF, an abstraction framework able to provide programmers with a simple but powerful API to ease the development of stream processing applications. By using SPAF, the programmer can easily implement real-time complex analyses of massive streams on top of a distributed computing infrastructure, able to manage the volume and velocity of Big Data streams, thus effectively transforming data into value. Frontiers Media S.A. 2023-10-25 /pmc/articles/PMC10634501/ /pubmed/37953916 http://dx.doi.org/10.3389/fdata.2023.1227156 Text en Copyright © 2023 Bartolini and Patella. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Big Data Bartolini, Ilaria Patella, Marco A stream processing abstraction framework |
title | A stream processing abstraction framework |
title_full | A stream processing abstraction framework |
title_fullStr | A stream processing abstraction framework |
title_full_unstemmed | A stream processing abstraction framework |
title_short | A stream processing abstraction framework |
title_sort | stream processing abstraction framework |
topic | Big Data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10634501/ https://www.ncbi.nlm.nih.gov/pubmed/37953916 http://dx.doi.org/10.3389/fdata.2023.1227156 |
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