Data Integration and Machine Learning: A Natural Synergy

被引:9
|
作者
Dong, Xin Luna [1 ]
Rekatsinas, Theodoros [2 ]
机构
[1] Amazon, Seattle, WA 98108 USA
[2] Univ Wisconsin, Madison, WI USA
来源
PROCEEDINGS OF THE VLDB ENDOWMENT | 2018年 / 11卷 / 12期
关键词
DATA FUSION; WEB;
D O I
10.14778/3229863.3229876
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
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.
引用
收藏
页码:2094 / 2097
页数:4
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