In this technology bound era, data analytics is a decisive way to deal with this enormous amount of data that is getting collected from various sources such as social media, banking, healthcare etc. With the growing volume of this data, it has been getting more and more difficult to analyze the same with the existing techniques. This is where the concept of Machine Learning (ML) has turned out to be an indispensable way for giving this data an intelligent structure i.e. by sorting the clusters of data into data sets and drawing associations from previous information. However, the traditional machine learning methods are not helpful in manipulating the data in a way that we require as we are advancing in these various fields involving big data. In our research we have reviewed the various ML algorithms and learning paradigms for handling the big data problems by associating them with the challenges of the 6 big data dimensions- Volume, Veracity, Velocity, Variety, Visualization and Value. We have studied the similar approach of research given by Alexandera et al. and Gandomi and Haider. Adding on to their findings and methods we have considered two more V’s – Visualization and Value and associated their characteristic challenges with the ML methods. We have mentioned the use of ML in preserving the privacy and security of the data as securing the data being generated is also a significant problem that needs to be addressed.

Big data analytics, Machine Learning, V’s of big data, algorithms, learning paradigms

[1] L. Sweeney, “k-anonymity: A model for protecting privacy,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 10, no. 05, 2002
[2] A.Vinothini and Dr.S.Baghavathi Priya “Survey of Machine Learning Methods for Big Data Applications” International Conference on Computational Intelligence in Data Science, 2017.
[3] Xi Fang and Juanjuan Wang, “The Application of Big Data Technology and Method in Moral Education in Colleges and a Universities”, International conference on Intelligent Transportation, Big data and Smart City (ICITBS)
[4] S. R. Sukumar, “Machine Learning in the Big Data Era: Are We There Yet?” in Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining: Workshop on Data Science for Social Good (KDD 2014), 2014.
[5] M. M. Najafabadi, F. Villanustre, T. M. Khoshgoftaar, N. Seliya, R. Wald, and E. Muharemagic, “Deep Learning Applications and Challenges in Big Data Analytics,” Journal of Big Data, vol. 2, Feb. 2015.
[6] J. Qiu, Q. Wu, G. Ding, Y. Xu, and S. Feng, “A Survey of Machine Learning for Big Data Processing,” EURASIP Journal on Advances in Signal Processing, vol. 67, 2016.
[7] J.Qiu et al., “A survey of machine learning for big data processing,” Signal Processing for Big Data, 28 May,2016
[8] O. Y. Al-Jarrah, P. D. Yoo, S. Muhaidat, G. K. Karagiannidis, and K. Taha, “Efficient Machine Learning for Big Data: A Review,” Big Data Research, vol. 2, no. 3, Apr. 2015.
[9] A. L`Heureux, K. Grolinger, H. F. Elyamany, and M. A. M. Capretz, “Machine Learning with Big Data: Challenges and Approaches,” IEEE Access, vol. 5.
[10] A. Gandomi and M. Haider, “Beyond the Hype: Big data Concepts, Methods, and Analytics,” International Journal of Information Management, vol. 35, Apr. 2015.
[11] S.Athmaja, M.Hanumanthapa and V.Kavitha, “A Survey Of Machine Learning Algorithms For Big Data Analytics,” International Confrence on Innovations in Information, Embedded Communication Systems(ICIIECS),2017
[12] H. Liu and H. Motoda, Instance Selection and Construction for Data Mining, vol. 608. Springer Science & Business Media, 2013.
[13] A. Buades, B. Coll, and J. Morel, “A Review of Image Denoising Algorithms, with a New One,” Multiscale Modeling & Simulation, vol. 4, no. 2, 2005.
[14] H. Park, R. Ikeda, and J. Widom, “RAMP: A System for Capturing and Tracing Provenance in MapReduce Workflows.,” Proceedings of the VLDB Endowment, vol. 4, no. 12, 2011.
[15] R. Agrawal and R. Srikant, “Privacy-preserving data mining,” in ACM Sigmod Record, vol. 29, no. 2. ACM, 2000.
[16] K. Liu, H. Kargupta, and J. Ryan, “Random projection-based multiplicative data perturbation for privacy preserving distributed data mining,” IEEE Transactions on knowledge and Data Engineering, vol. 18, no. 1, 2006.
[17] Seref Sagiroglu and Duygu Sinanc, “Big Data: A Review”, IEEE jornal,2013
[18] A. Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramaniam, “l-diversity: Privacy beyond k-anonymity,” ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 1, 2007.
[19] R. Salakhutdinov and G. E. Hinton, “Deep Boltzmann Machines,” in Proceedings of the International Conference on Artificial Intelligence and Statistics, 2009.
[20] G. Hinton, “Deep Belief Nets,” in Encyclopedia of Machine Learning, Springer, 2010, pp. 267–269.
[21] CW Tsai, CF Lai, MC Chiang, LT Yang, “Data mining for the internet of things: a survey”. IEEE Commun Surv Tut 16(1), 77–97 (2014)
[22] X Wu, X Zhu, G Wu, W Ding, “Data mining with big data”. IEEE Trans Knowl Data Eng 26(1), 97–107 (2014)
[23] U Fayyad, G Piatetsky-Shapiro, P Smyth, “From data mining to knowledge discovery in databases”. AI Mag 17(3), 37–54 (1996)
[24] Mohammed Z. Omer and Hui Gao, “Privacy Preserving in Distributed SVM Data Mining on Vertical Partitioned Data”, 3rd International Conference on Soft Computing & Machine Intelligence, 2016.
[25] M. M. Najafabadi, F. Villanustre, T. M. Khoshgoftaar, N. Seliya, R. Wald, and E. Muharemagic, “Deep Learning Applications and Challenges in Big Data Analytics,” Journal of Big Data, Feb. 2015.
[26] C. L. Philip Chen and C. Y. Zhang, “Data-Intensive Applications, Challenges, Techniques and Technologies: a Survey on Big Data,” Information Sciences, vol. 275, 2014.
[27] S. G. Teo, S. Han, and V. C. Lee, “Privacy preserving support vector machine using non-linear kernels on hadoop mahout,” in Computational Science and Engineering (CSE), 2013 IEEE 16th International Conference on. IEEE, 2013, pp. 941–948.
[28] Y. Rahulamathavan, S.Veluru, R. C.-W. Phan, J. A. Chambers, and M. Rajarajan, “Privacy-preserving clinical decision support system using gaussian kernel-based classification,” IEEE journal of biomedical and health informatics, vol. 18, no. 1, pp. 56–66, 2014.
[29] Long Xu and Yihua Yan, “Machine Learning for Astronomical Big Data Processing”, IEEE,2017
[30] A.Vinothini, S.Baghavati, “Survey of Machine Learning for Big Data Applications,” International Confrence on Computational Intelligence in Data Science(ICCIDS),2017
[31] C.Augenstein, N.Spangenberg and Bogdan Franczyk, “Applying Machine Learning to Big Data Streams,”4th IEEE International Conference on Soft Computing and Machine Intelligence,2017