将机器学习应用于大数据流:挑战概述

2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2017-11-01 DOI:10.1109/ISCMI.2017.8279592

Christoph Augenstein, N. Spangenberg, Bogdan Franczyk

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引用次数: 15

摘要

处理流数据的重要性随着新技术和新用例的出现而增加。将机器学习应用于数据流并实时处理它们会带来不同方面的挑战。模型变化、概念漂移或训练模型的时间不足都是一些例子。我们从文献中阐述了大数据的特征和机器学习技术，并总结了可用的方法和缺点。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Applying machine learning to big data streams : An overview of challenges

The importance of processing stream data increases with new technologies and new use cases. Applying machine learning to stream data and process them in real time leads to challenges in different ways. Model changes, concept drift or insufficient time to train models are a few examples. We illustrate big data characteristics and machine learning techniques derived from literature and conclude with available approaches and drawbacks.

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2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI)

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