MATHEMATICAL STRUCTURE THEORY AS A SOURCE FOR BIG DATA SCIENCE

Authors:

MD Mobin Akhtar,Danish Ahamad,Ahmed Marzouq Alotaibi,

DOI NO:

https://doi.org/10.26782/jmcms.2020.01.00016

Keywords:

Big data,mathematical modelling,big data analysis,big data computing,

Abstract

The recent expansion of research into big data has set an exciting goal for mathematicians, Computer scientists as well as business professionals. Though, the absence of a Sound architecture of mathematics presents itself by way of a actual experiment in the Big Data advancement community. The paper's goal is to propose a possible theory of mathematical structure as per a basis of research into big data. The analysis of the application a mathematical modelling can be strongly wellthought- out as a theory of the Big data transforming technologies, systems, data management and processing tools. In amassing, the premise of big data's inanity is constructed on the calculus & principle and set theory. Its suggested method in this paper, encourage and open up more open doors for large information research and advancements on Big data information knowledge, business analytics, big data information investigation, big data Computing information technology as well as big data Computer science.

Refference:

I. Gandomi and M. Haider, Beyond the hype: Big data concepts, methods, and
analytics, International Journal of Information Management 35 (2015) 137-144.
II. A. McAfee and E. Brynjolfsson, Big data: The management revolution, Harvard
Business Review 90(10) (2012)
III. C. K. Chui and Q. Jiang, Applied Mathematics: Data Compression, Spectral
Methods, Fourier Analysis, Wavelets, and Applications (Springer, 2013).
IV. C. K. Chui and Q. Jiang, Applied Mathematics: Data Compression, Spectral
Methods, Fourier Analysis, Wavelets, and Applications (Springer, 2013).
V. C. P.iChen and C.-Y. Zhang, Data-intensive applications, challenges,techniques
and technologies: A survey on Big Data, Information Sciences 275 (2014).
VI. C. iCoronel, S. Morris and P. Rob, Database Systems: Designs, Implementation,
and Management, 11th edn. (Course Technology, Cengage Learning, Boston,
2015).
VII. C. P.iChen and C.-Y. Zhang, Data-intensive applications, challenges,techniques
and technologies: A survey on Big Data, Information Sciences 275 (2014).
VIII. IBM, The Four V’s of Big Data (2015),ii
http://www.ibmbigdatahubi.com/infographic/ four-vs-bigdata.
IX. L. A. Zadeh, Fuzzy sets and information granularity, in Advances in Fuzzy Sets
Theory and Applications, eds. M. Gupta, R. K. Ragade and R. R. Yager (North-
Holland, New York, 1979),
X. L. A. Zadeh, Fuzzy sets, Information and Control 8(3) (1965)
XI. M. Kantardzic, Data Mining: Concepts, Models, Methods, and Algorithms
(Wiley & IEEE Press, Hoboken, 2011).
XII. M. Minellii, M. Chamber and A. Dhiras, Big Data, Big Analytics: Emerging
Business Intelligence and Analytic Trends for Today’s Businesses (John Wiley
and Sons, 2013).
J. Mech. Cont.& Math. Sci., Vol.-15, No.-1, January (2020) pp 209-216
Copyright reserved © J. Mech. Cont.& Math. Sci.
MD Mobin Akhtar et al
216
XIII. M. Minelli, M. Chambers and A. Dhiraj, Big Data, Big Analytics: Emerging
Business Intelligence and Analytic Trends for Today’s Businesses, Chinese edn.
(Wiley & Sons, 2013)
XIV. R. Larson and B. H. Edwards, Calculus, 9th edn. (Brooks Cole Cengage
Learning, 2010).
XV. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 3rd edn.
(Prentice Hall, Upper Saddle River, 2010).
XVI. Z. Sun and G. Finnie, Experience management in knowledge management, in
KES 2005: Knowledge-Based Intelligent Information and Engineering Systems,
LNCS, Vol. 3681 (Springer-Verlag, Berlin, 2005)
XVII. Z. Sun and J. Xiao, Essentials of Discrete Mathematics,i Problems and
Solutionsi (Hebei University Press, Baoding, 1994).

View Download