A Survey on Deep Learning in Big Data

被引：88
|
作者：
Gheisari, Mehdi [1 ]
Wang, Guojun [1 ]
Bhuiyan, Md Zakirul Alam [1 ,2 ]
机构：
[1] Guangzhou Univ, Sch Comp Sci & Educ Software, Guangzhou 510006, Guangdong, Peoples R China
[2] Fordham Univ, Dept Comp & Informat Sci, New York, NY 10458 USA
来源：
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING (CSE) AND IEEE/IFIP INTERNATIONAL CONFERENCE ON EMBEDDED AND UBIQUITOUS COMPUTING (EUC), VOL 2 | 2017年
基金：
中国国家自然科学基金;
关键词：
Big Data; Deep learning; Deep Learning Challenges; Machine Learning; Deep Learning Methods; Big Data Challenges; CHALLENGES; OPPORTUNITIES; INTELLIGENCE; STRATEGY; CLOUD;
D O I：
10.1109/CSE-EUC.2017.215
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Big Data means extremely huge large data sets that can be analyzed to find patterns, trends. One technique that can be used for data analysis so that able to help us find abstract patterns in Big Data is Deep Learning. If we apply Deep Learning to Big Data, we can find unknown and useful patterns that were impossible so far. With the help of Deep Learning, AI is getting smart. There is a hypothesis in this regard, the more data, the more abstract knowledge. So a handy survey of Big Data, Deep Learning and its application in Big Data is necessary. In this paper, we provide a comprehensive survey on what is Big Data, comparing methods, its research problems, and trends. Then a survey of Deep Learning, its methods, comparison of frameworks, and algorithms is presented. And at last, application of Deep Learning in Big Data, its challenges, open research problems and future trends are presented.
引用
收藏
页码：173 / 180
页数：8
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