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
相关论文
共 50 条
  • [31] Is Prompt the Future? A Survey of Evolution of Relation Extraction Approach Using Deep Learning and Big Data
    Zhu, Zhen
    Wang, Liting
    Gu, Dongmei
    Wu, Hong
    Janfada, Behrooz
    Minaei-Bidgoli, Behrouz
    INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGIES AND SYSTEMS APPROACH, 2023, 16 (01) : 1172 - 1189
  • [32] Big Data, Data Mining, Machine Learning, and Deep Learning Concepts in Crime Data
    Ates, Emre Cihan
    Bostanci, Erkan
    Guzel, Mehmet Serdar
    JOURNAL OF PENAL LAW AND CRIMINOLOGY-CEZA HUKUKU VE KRIMINOLOJI DERGISI, 2020, 8 (02): : 293 - 319
  • [33] Survey of Machine Learning Methods for Big Data Applications
    Vinothini, A.
    Priya, S. Baghavathi
    2017 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE IN DATA SCIENCE (ICCIDS), 2017,
  • [34] Systematic Survey on Evolution of Machine Learning for Big Data
    Swathi, R.
    Seshadri, R.
    2017 INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND CONTROL SYSTEMS (ICICCS), 2017, : 204 - 209
  • [35] Online learning algorithms for big data analytics: A survey
    Li, Zhijie
    Li, Yuanxiang
    Wang, Feng
    He, Guoliang
    Kuang, Li
    Jisuanji Yanjiu yu Fazhan/Computer Research and Development, 2015, 52 (08): : 1707 - 1721
  • [36] Learning to Hash for Indexing Big Data-A Survey
    Wang, Jun
    Liu, Wei
    Kumar, Sanjiv
    Chang, Shih-Fu
    PROCEEDINGS OF THE IEEE, 2016, 104 (01) : 34 - 57
  • [37] Erratum to: A survey of machine learning for big data processing
    Junfei Qiu
    Qihui Wu
    Guoru Ding
    Yuhua Xu
    Shuo Feng
    EURASIP Journal on Advances in Signal Processing, 2016
  • [38] A SURVEY OF MACHINE LEARNING ALGORITHMS FOR BIG DATA ANALYTICS
    Athmaja, S.
    Hanumanthappa, M.
    Kavitha, Vasantha
    2017 INTERNATIONAL CONFERENCE ON INNOVATIONS IN INFORMATION, EMBEDDED AND COMMUNICATION SYSTEMS (ICIIECS), 2017,
  • [39] Deep learning applications and challenges in big data analytics
    Najafabadi M.M.
    Villanustre F.
    Khoshgoftaar T.M.
    Seliya N.
    Wald R.
    Muharemagic E.
    Journal of Big Data, 2 (1)
  • [40] Deep learning in big data Analytics: A comparative study
    Jan, Bilal
    Farman, Haleem
    Khan, Murad
    Imran, Muhammad
    Ul Islam, Ihtesham
    Ahmad, Awais
    Ali, Shaukat
    Jeon, Gwanggil
    COMPUTERS & ELECTRICAL ENGINEERING, 2019, 75 : 275 - 287
12345