BigData on Machine Learning – A Review

Author
K. Balasree, Dr. K. Dharmarajan
Keywords
Big Data; Analytics; Machine Learning; SVM; Decision Tree; Naïve Baye’s; Random Forest.
Abstract
In rapid development of Big Data technology over the recentyears, this paper discussing about the Machine Learning (ML) playing role that is based on methods and algorithms to Big Data Processing and BigData Analytics. In evolutionary fields and computing fields of developments that both are complementing each other. Big Data: Therapid growth of such data solutions needed to be studied and providedtohandlethentogainthe knowledge fromdatasetsand extractingvaluesdue to the data sets are very high in velocity and variety. The Big dataanalytics are involving and indicating the appropriate data storage and computational outline that enhanced by using Scalable Machine Learning Algorithms and BigData Analytics then the analytics to reveal the massive amounts of hidden data’s and secret correlations. This typeof Analytic information useful for organizations and companies to gain deeper knowledge, development and getting advantages over the competition. When using this Analytics we can predict the accurate implementation over the data. This paper presented about the detailed review of state-of-the-art developments and overview of advantages and challenges in Machine Learning Algorithms over bigdata analytics.
References
[1] Al-Jarrah, Omar Y., et al. “Efficient machine learning for big data: A review.” Big Data Research 2.3 (2015): 87-93.
[2] Hossain, Eklas, et al. “Application of big data and machine learning in smart grid, and associated security concerns: A review.” IEEE Access 7 (2019): 13960-13988.
[3] Bhatnagar, Roheet. “Machine Learning and Big Data processing: a technological perspective andreview.” International Conference on Advanced Machine Learning Technologies and Applications. Springer, Cham, 2018.
[4] Gupta, Preeti, Arun Sharma, and Rajni Jindal. “Scalable machine‐learning algorithms for big data analytics: a comprehensive review.” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 6.6 (2016): 194- 214.
[5] Sagiroglu, Seref, and DuyguSinanc. “Big data: A review.” 2013 international conference on collaboration technologies and systems (CTS). IEEE, 2013.
[6] George, Gerard, Martine R. Haas, and Alex Pentland. “Big data and management.” (2014): 321-326.
[7] Ngiam, Kee Yuan, and Wei Khor. “Big data and machine learning algorithms for health-care delivery.” The Lancet Oncology 20.5 (2019): e262-e273.
[8] Qiu, Junfei, et al. “A survey of machine learning for big data processing.” EURASIP Journal on Advances in Signal Processing 2016.1 (2016): 67.
[9] Angra, Sheena, and Sachin Ahuja. “Machine learning and its applications: A review.” 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC). IEEE, 2017.
[10] Al-Jarrah, Omar Y., et al. “Efficient machine learning for big data: A review.” Big Data Research 2.3 (2015): 87-93.
[11] Hossain, Eklas, et al. “Application of big data and machine learning in smart grid, and associated security concerns: A review.” IEEE Access 7 (2019): 13960-13988.
[12] Ma, Chuang, Hao Helen Zhang, and Xiangfeng Wang. “Machine learning for Big Data analytics in plants.” Trends in plant science 19.12 (2014): 798-808.
[13] Miklosik, Andrej, and Nina Evans. “Impact of big data and machine learning on digital transformation in marketing: A literature review.” IEEE Access (2020).
[14] L’heureux, Alexandra, et al. “Machine learning with big data: Challenges and approaches.” IEEE Access 5 (2017): 7776-7797.
[15] Salkuti, Surender Reddy. “A survey of big data and machine learning.” International Journal of Electrical & Computer Engineering (2088-8708) 10 (2020).
[16] Zhou, Lina, et al. “Machine learning on big data: Opportunities and challenges.” Neurocomputing 237 (2017): 350-361.
[17] Divya, K. Sree, PeyakuntaBhargavi, and S. Jyothi. “Machine learning algorithms in big data analytics.” International Journal of Computer Sciences and Engineering 6.1 (2018): 64-70.
[18] Beam, Andrew L., and Isaac S. Kohane. “Big data and machine learning in health care.” Jama 319.13 (2018): 1317-1318.
[19] Ning, Chao, and Fengqi You. “Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming.” Computers & Chemical Engineering 125 (2019): 434-448.
[20] Tu, Chunming, et al. “Big data issues in smart grid–A review.” Renewable and Sustainable Energy Reviews 79 (2017): 1099-1107.
[21] Suthaharan, Shan. “Big data classification: Problems and challenges in network intrusion prediction with machine learning.” ACM SIGMETRICS Performance Evaluation Review 41.4 (2014): 70-73.
[22 Zuo, Renguang, and YihuiXiong. “Big data analytics of identifying geochemical anomalies supported by machine learning methods.” Natural Resources Research 27.1 (2018): 5-13.
[23] Voyant, Cyril, et al. “Machine learning methods for solar radiation forecasting: A review.” Renewable Energy 105 (2017): 569-582.
[24] Cabitza, Federico, Angela Locoro, and Giuseppe Banfi. “Machine learning in orthopedics: a literature review.” Frontiers in bioengineering and biotechnology 6 (2018): 75.
[25] Manogaran, Gunasekaran, and Daphne Lopez. “A survey of big data architectures and machine learning algorithms in healthcare.” International Journal of Biomedical Engineering and Technology 25.2-4 (2017): 182-211.

Received :21 May 2021
Accepted :14 September 2021
Published :26 September 2021
DOI: 10.30726/esij/v8.i3.2021.83018

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