A Survey on Big Data Analytics: Challenges

  • Sundeep Kumar Awasthi
Keywords: Big Data Analytics, Data Storage and Analysis, Knowledge Discovery and Computational Complexities, Scalability and Visualization of Data


A gigantic archive of terabytes of information is created every day from current data frameworks and computerized advances, for example, Internet of Things and distributed computing. Examination of these gigantic information requires a ton of endeavors at various levels to extricate information for dynamic. Hence, huge information examination is an ebb and flow region of innovative work. The essential goal of this paper is to investigate the likely effect of huge information challenges, and different instruments related with it. Accordingly, this article gives a stage to investigate enormous information at various stages. Moreover, it opens another skyline for analysts to build up the arrangement, in light of the difficulties and open exploration issues.


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M. K.Kakhani, S. Kakhani, & S. R.Biradar. (2015). Research issues in big data analytics. International Journal of Application or Innovation in Engineering & Management, 2(8), 228-232.

A. Gandomi & M. Haider. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2) 137-144.

C. Lynch. (2008). Big data: How do your data grow?, Nature, 455, 28-29.

X. Jin, B. W.Wah, X. Cheng, & Y. Wang. (2015). Significance and challenges of big data research. Big Data Research, 2(2), 59-64.

R. Kitchin. (2014). Big data, new epistemologies and paradigm shifts. Big Data Society, 1(1), 1-12.

C. L. Philip, Q. Chen, & C. Y. Zhang. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on big data. Information Sciences, 275, 314-347.

K. Kambatla, G. Kollias, V. Kumar, & A. Gram. (2014). Trends in big data analytics, Journal of Parallel and Distributed Computing, 74(7), 2561-2573.

S. Del. Rio, V. Lopez, J. M. Bentez, & F. Herrera. (2014). On the use of mapreduce for imbalanced big data using random forest. Information Sciences, 285, 112-137.

MH. Kuo, T. Sahama, A. W. Kushniruk, E. M. Borycki, & D. K. Grunwell. (2014). Health big data analytics: current perspectives, challenges and potential solutions. International Journal of Big Data Intelligence, 1, 114-126.

R. Nambiar, A. Sethi, R. Bhardwaj, & R. Vargheese. (2013). A look at challenges and opportunities of big data analytics in healthcare. In: IEEE International Conference on Big Data, pp.17-22.

Z. Huang. (1997). A fast clustering algorithm to cluster very large categorical data sets in data mining. SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery.

T. K. Das & P. M. Kumar. (2013). Big data analytics: A framework for unstructured data analysis. International Journal of Engineering and Technology, 5(1), 153-156.

T. K. Das, D. P. Acharjya, & M. R. Patra. (2014). Opinion mining about a product by analyzing public tweets in twitter. In: International Conference on Computer Communication and Informatics.

L. A. Zadeh. (1965). Fuzzy sets. Information and Control, 8, 338353.

Z. Pawlak. (1982). Rough sets, International Journal of Computer Information Science, 11, 341-356.

D. Molodtsov. (1999). Soft set theory first results. Computers and Mathematics with Applications, 37(4/5), 19-31.

J. F.Peters. (2007). Near sets. General theory about nearness of objects. Applied Mathematical Sciences, 1(53), 2609-2629.

R. Wille. (2005). Formal concept analysis as mathematical theory of concept and concept hierarchies. Lecture Notes in Artificial Intelligence, 3626, 1-33.

I. T. Jolliffe. (2002). Principal component analysis. New York: Springer.

O. Y. Al-Jarrah, P. D. Yoo, S. Muhaidat, G. K. Karagiannidis, & K. Taha. (2015). Efficient machine learning for big data: A review. Big Data Research, 2(3), 87-93.

Changwon. Y, Luis. Ramirez, & Juan. Liuzzi. (2014). Big data analysis using modern statistical and machine learning methods in medicine. International Neurourology Journal, 18, 50-57.

P. Singh & B. Suri. (2014). Quality assessment of data using statistical and machine learning methods. L. C.Jain, H. S.Behera, J. K.Mandal, & D. P.Mohapatra (eds.), Computational Intelligence in Data Mining, 2, pp. 89-97.

A. Jacobs. (2009). The pathologies of big data. Communications of the ACM, 52(8), 36-44.

How to Cite
Sundeep Kumar Awasthi. (2020). A Survey on Big Data Analytics: Challenges. International Journal of Engineering and Management Research, 10(4), 114-117. https://doi.org/10.31033/ijemr.10.4.17