Efficient Deep Learning for Big Data: A Review

Prakash Singh¹

Section:Research Paper, Product Type: Isroset-Journal
Vol.4 , Issue.6 , pp.36-41, Dec-2016

Online published on Dec 06, 2016

Copyright © Prakash Singh . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
The data science is composed of Big Data Analytics (BDA) and Deep Learning (DL). Apart from this Big Data (BD) has got popularity due to its importance in the present genre for both the public and private organizations, as this applies to collection of huge data. Basically, the BD is composed of many national intelligence applications, medical technology, cyber security data, etc. Many of the companies are analyzing the BD for its business purpose. The DL is the sequential or active learning process which collects the complex data and high-level data. This DL has its own beneficial key functions like learning and analysis of the enormous volume of unsupervised data (UD). This performs as the most valuable data analytics tool for BDA. In this paper, a brief overview of Deep learning in Big Data Analytics is presented with the challenges of DL in BD. The statistical survey is formulated by using IEEExplore. Finally, the paper future study requirements in Deep learning are discussed.

Key-Words / Index Term :
Big Data, Big Data Analytics, Deep Learning, Machine Learning, Unsupervised Data

References :
[1] Sato, Aki-Hiro. Applied Data-Centric Social Sciences. Springer, 2014.
[2] Ballard, Chuck, “Information Governance Principles and Practices for a Big Data Landscape”, IBM Redbooks, US, 2014.
[3] Han, Jiawei, Micheline Kamber, and Jian Pei. “Data mining: concepts and techniques”, Elsevier, US, 2011.
[4] Hand, David J., Heikki Mannila, and Padhraic Smyth, “Principles of data mining”, MIT Press, US, 2001.

[5] Carbonell, Jaime G., Ryszard S. Michalski, and Tom M. Mitchell, "An overview of machine learning", Machine learning. Springer Berlin Heidelberg, pp. 3-23, 1983.
[6] Suthaharan, Shan. "Big Data Analytics," Machine Learning Models and Algorithms for Big Data Classification”, Springer US, pp. 31-75, 2016.
[7] LaValle, Steve, et al. "Big data, analytics and the path from insights to value," MIT Sloan management review, Vol.52, Issue.2, pp.21-30, 2011.

[8] Arel, Itamar, Derek C. Rose, and Thomas P. Karnowski. "Deep machine learning-a new frontier in artificial intelligence research [research frontier]", Computational Intelligence Magazine, Vol. 5, Issue.4, pp.13-18, 2010.
[9] LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature, Vol.521, Issue.7553, pp. 436-444, 2015.
[10] Najafabadi, Maryam M., “Deep learning applications and challenges in big data analytics", Journal of Big Data, Vol. 2, Issue.1, pp. 1-21, 2015.
[11] Chen, CL Philip, and Chun-Yang Zhang. "Data-intensive applications, challenges, techniques, and technologies: A survey of Big Data", Information Sciences, Vol.275, pp. 314-347, 2014.
[12] MapReduce," in Access, IEEE, vol.2, no., pp.395-403, 2014.
[13] Xue-Wen Chen; Xiaodong Lin, "Big Data Deep Learning: Challenges and Perspectives", IEEE Access, Vol.2, no.6, pp.514-525, 2014

[14] Fei Wu; Zhuhao Wang; Zhongfei Zhang; Yi Yang; Jiebo Luo; Wenwu Zhu; Yueting Zhuang, "Weakly Semi-Supervised Deep Learning for Multi-Label Image Annotation", in Big Data, IEEE Transactions on, vol.1, no.3, pp.109-122, Sept. 1, 2015

[15] Weishan Zhang; Pengcheng Duan; Zhongwei Li; Qinghua Lu; Wenjuan Gong; Su Yang, "A Deep Awareness Framework for Pervasive Video Cloud", IEEE Access, vol.3, no.6, pp.2227-2237, 2015.
[16] Hongming Zhou; Guang-Bin Huang; Zhiping Lin; Han Wang; Yeng Chai Soh, "Stacked Extreme Learning Machines", EEE Transactions in Cybernetics, vol.45, no.9, pp.2013-2025, 2015.
[17] Yisheng Lv; Yanjie Duan; Wenwen Kang; Zhengxi Li; Fei-Yue Wang, "Traffic Flow Prediction With Big Data: A Deep Learning Approach", , IEEE Transactions on Intelligent Transportation Systems, vol.16, no.2, pp.865-873, 2015.

[18] Park, S.-W.; Park, J.; Bong, K.; Shin, D.; Lee, J.; Choi, S.; Yoo, H.-J., "An Energy-Efficient and Scalable Deep Learning/Inference Processor With Tetra-Parallel MIMD Architecture for Big Data Applications",IEEE Transactions on Biomedical Circuits and Systems, vol.9, no.6, pp.838-848, 2015.
[19] Jun Wang; Wei Liu; Kumar, S.; Shih-Fu Chang, "Learning to Hash for Indexing Big Data—A Survey", Proceedings of the IEEE, vol.104, no.1, pp.34-57, 2016.
[20] Zhang, Q.; Yang, L.T.; Chen, Z., "Deep Computation Model for Unsupervised Feature Learning on Big Data", IEEE Transactions on Services Computing, vol.9, no.1, pp.161-171, 2016.
[21] Leung, M.K.K.; Delong, A.; Alipanahi, B.; Frey, B.J., "Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets",Proceedings of the IEEE, vol.104, no.1, pp.176-197, 2016.