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Data Integration and Machine Learning: A Natural Synergy

机译:数据集成与机器学习:自然协同作用

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摘要

As data volume and variety have increased, so have the ties between machine learning and data integration become stronger. For machine learning to be effective, one must utilize data from the greatest possible variety of sources; and this is why data integration plays a key role. At the same time machine learning is driving automation in data integration, resulting in overall reduction of integration costs and improved accuracy. This tutorial focuses on three aspects of the synergistic relationship between data integration and machine learning: (1) we survey how state-of-the-art data integration solutions rely on machine learning-based approaches for accurate results and effective human-in-the-loop pipelines, (2) we review how end-to-end machine learning applications rely on data integration to identify accurate, clean, and relevant data for their analytics exercises, and (3) we discuss open research challenges and opportunities that span across data integration and machine learning.
机译:随着数据量和多样性的增加,机器学习与数据集成之间的关系变得更强大。 对于机器学习有效,必须利用来自最大种类的来源的数据; 这就是为什么数据集成播放关键角色的原因。 同时机器学习是在数据集成中驾驶自动化,导致整体降低集成成本和提高准确性。 本教程专注于数据集成和机器学习之间协同关系的三个方面:(1)我们调查了最先进的数据集成解决方案如何依赖基于机器学习的方法,以获得准确的结果和有效的人 -Loop管道,(2)我们介绍端到端机器学习应用程序如何依赖于数据集成来识别其分析练习的准确,干净和相关数据,以及我们讨论开放的研究挑战和跨越的机会 数据集成和机器学习。

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