In the recent years, the database on the cloud network is increased exponentially and this enormous volume of data is known as Big Data. The Big Data is described in five V’s known as volume, velocity, variety, Veracity, and Value. Hence, efficient algorithms and architectures are required to process and store the data. In this paper, a review study on machine learning techniques for database management is done. From the study, it is found that machine-learning algorithms provide efficient data processing and storage. In the last research issues are defined which helps the other author to contribute their work in this area.

Big Data, Machine Learning, Internet of Things, Database Management

[1] Athmaja, S., Hanumanthappa, M. and Kavitha, V, “A survey of machine learning algorithms for big data analytics,” In Innovations in Information, Embedded and Communication Systems (ICIIECS), 2017 International Conference, pp. 1-4, 2017.
[2] Raghupathi, W. and Raghupathi, V., “Big data analytics in healthcare: promise and potential,” Health information science and systems, vol. 2, issue 1, p.3, 2014
[3] Suthaharan, S., “Big data classification: Problems and challenges in network intrusion prediction with machine learning,” ACM SIGMETRICS Performance Evaluation Review, vol. 41, pp.70-73, 2014
[4] Wei Tan, Brian Blake, Iman Saleh, and Schahram Dustar, “Social-Networks-Sourced Big Data Analytics,” IEEE Internet Computing, vol. 17, issue 5, pp.62-69, 2013.
[5] Edosio, U.Z., “Big data Analytics and its Application in E-commerce,” Proceedings E-Commerce Technologies. University of Bradford, 2014.
[6] Singh, S.P. and Jaiswal, U.C., “Machine Learning for Big Data: A New Perspective,” International Journal of Applied Engineering Research, vol. 13, issue 5, pp.2753-2762, 2018.
[7] Zhou, L., Pan, S., Wang, J. and Vasilakos, A.V., “Machine learning on big data: Opportunities and challenges,” Neurocomputing, vol. 237, pp.350-361, 2017.
[8] Swathi, R. and Seshadri, R., “Systematic survey on evolution of machine learning for big data,” In Intelligent Computing and Control Systems (ICICCS), 2017 International Conference on, pp. 204-209, 2017.
[9] Zhou, L., Pan, S., Wang, J. and Vasilakos, A.V., Machine learning on big data: Opportunities and challenges. Neurocomputing, vol. 237, pp.350-361, 2017.
[10] Varatharajan, R., Manogaran, G. and Priyan, M.K., “A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing,” Multimedia Tools and Applications, vol. 77, issue 8 , pp.10195-10215, 2018.
[11] Maillo, J., Ramírez, S., Triguero, I. and Herrera, F.,. “KNN-IS: An Iterative Spark-based design of the k-Nearest Neighbors classifier for big data,” Knowledge-Based Systems, vol. 117, pp.3-15, 2017.
[12] Chen, M., Hao, Y., Hwang, K., Wang, L. and Wang, L.,. “Disease prediction by machine learning over big data from healthcare communities,” IEEE Access, vol. 5, pp.8869-8879, 2017
[13] Sun, F., Huang, G.B., Wu, Q.J., Song, S. and Wunsch II, D.C.,. “Efficient and rapid machine learning algorithms for big data and dynamic varying systems,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, issue 10, pp.2625-2626, 2017.
[14] Le, L.V., Sinh, D., Tung, L.P. and Lin, B.S.P., “A practical model for traffic forecasting based on big data, machine-learning, and network KPIs,” In Consumer Communications & Networking Conference (CCNC), 2018 15th IEEE Annual, pp. 1-4, 2018.
[15] Li, P., Chen, Z., Yang, L.T., Zhang, Q. and Deen, M.J., “Deep convolutional computation model for feature learning on big data in Internet of Things” IEEE Transactions on Industrial Informatics, vol. 14, issue 2, pp.790-798, 2018.
[16] Shen, Y., Han, T., Yang, Q., Yang, X., Wang, Y., Li, F. and Wen, H., “CS-CNN: Enabling Robust and Efficient Convolutional Neural Networks Inference for Internet-of-Things Applications,” IEEE Access, vol. 6, pp.13439-13448, 2018.
[17] Lakshmanaprabu, S.K., Shankar, K., Khanna, A., Gupta, D., Rodrigues, J.J., Pinheiro, P.R. and De Albuquerque, V.H.C., “Effective Features to Classify Big Data Using Social Internet of Things,” IEEE Access, vol. 6, pp.24196-24204, 2018.