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A framework for Internet data real-time processing: A machine-learning approach

机译：Internet数据实时处理的框架：一种机器学习方法

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摘要

Nowadays, the Internet Service Providers have to keep track of and in some cases to analyze for legal issues, a great amount of Internet data. Real-time big data processing and analysis introduce new challenges that must be addressed by system engineers. This is because: 1) traditional technologies exploiting databases are not designed to process a huge amount of data in real-time 2) classic machine learning algorithms implemented by widely adopted tools as Weka or R are not designed to perform “on the fly” analysis on streamed data. In this paper the authors propose an architecture that makes the real-time big data processing and analysis possible. The proposed architecture is based on two main components: a stream processing engine called Apache Storm and a framework called Yahoo SAMOA allowing to perform data analysis through distributed streaming machine learning algorithms. Our architecture is tested for Skype traffic recognition within network traffic generated by several Personal Computers in a streamed way. Experimental results have shown the effectiveness of proposed solution.
机译：如今，Internet服务提供商必须跟踪大量的Internet数据，并在某些情况下分析法律问题，以分析法律问题。实时大数据处理和分析带来了系统工程师必须解决的新挑战。这是因为：1）利用数据库的传统技术并未设计为能够实时处理大量数据。2）由于Weka或R并未被广泛采用的工具实现的经典机器学习算法并非旨在进行“即时”分析。流数据。在本文中，作者提出了一种使实时大数据处理和分析成为可能的体系结构。提议的体系结构基于两个主要组件：称为Apache Storm的流处理引擎和名为Yahoo SAMOA的框架，该框架允许通过分布式流式机器学习算法执行数据分析。我们的体系结构已经过测试，可以通过流方式在多台个人计算机生成的网络流量中进行Skype流量识别。实验结果表明了所提出解决方案的有效性。

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