一个流处理抽象框架。

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Frontiers in Big Data Pub Date : 2023-10-25 eCollection Date: 2023-01-01 DOI:10.3389/fdata.2023.1227156
Ilaria Bartolini, Marco Patella
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引用次数: 0

摘要

现在,大型多媒体流的实时分析由于几个大数据流平台的存在而变得高效,比如Apache Flink和Samza。然而,使用这些平台是困难的,因为它们提供的设施往往太原始,无法被分析师有效利用。我们描述了RAM3S(一种集成大数据流处理平台的软件基础设施)到SPAF(一种抽象框架,能够为程序员提供简单但功能强大的API,以简化流处理应用程序的开发)的演变。通过使用SPAF,程序员可以轻松地在分布式计算基础设施之上实现对海量流的实时复杂分析,能够管理大数据流的数量和速度,从而有效地将数据转化为价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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.

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来源期刊
CiteScore
5.20
自引率
3.20%
发文量
122
审稿时长
13 weeks
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