Welcome to Francis Academic Press

International Journal of Frontiers in Sociology, 2020, 2(7); doi: 10.25236/IJFS.2020.020710.

Application of Big Data technology in Enterprise Management

Author(s)

Chao Zhang*

Corresponding Author:
Chao Zhang
Affiliation(s)

Department of Finance and Economics,Chengdu Polytechnic,Sichuan,China
*Corresponding author: 56142797@qq.com

Abstract

Big data is quietly changing our world; all walks of life are using big data. Big data can provide decision-making basis for the government, enterprises, research and development, so it is very necessary to master the correct big data analysis method and intelligent, in-depth and valuable information extraction! Shannon, the father of information theory, once said that information is used to eliminate mistrust, such as predicting whether it will rain tomorrow. If you know today's weather, wind speed, clouds, air pressure and other information, it will help to draw more accurate conclusions. Therefore, big data is used to eliminate uncertainty and master more effective data, which can drive enterprises to make scientific and objective decisions.

Keywords

big data; enterprise management; informatization

Cite This Paper

Chao Zhang. Application of Big Data technology in Enterprise Management. International Journal of Frontiers in Sociology (2020), Vol. 2, Issue 7: 84-91. https://doi.org/10.25236/IJFS.2020.020710.

References

[1] Souiden Imen, Brahmi Zaki (2017). A survey on outlier detection in the context of  stream mining. 16th International Conference on Intelligent Systems Design and Applications,p.363-372.
[2] Tao G, Yan Y G, Liu J, and Zou J (2015). Discrimination of continuous attributes based on improved SOM clustering. Journal of Computer Applications, p.83-87.
[3] He Ming and Ren Wanpeng (2017). Attribute Reduction with Rough Set in Context-Aware Collaborative Filtering, China Journal of Electronics,p.24-25.
[4] Yu Zhanqiu (2019). Big data clustering analysis algorithm for internet of things based on K-means. International Journal of Distributed Systems and Technologies, p.3-7.
[5] Portugal Ivens, Alencar Paulo, Cowan Donald (2018). The use of machine learning algorithms in recommender systems: A systematic review. Expert Systems with Applications ,p.212-214.
[6] H Wang, SL Hung (2012). Phylogenetic tree selection by the adjusted k-means approach. Journal of Applied Statistics, p.655-657.
[7] Zhang R G, Hu X H, Zong Y S (2017). Discretization of continuous attributes based on improved discrete particle swarm optimization. Computer Engineering and Applications,p.101-104.
[8] Jonathan L, Shyam V, Vanathi G (2011). Application of an efficient Bayesian discretization method to biomedical data. BMC Bioinformatics, p.1410-1610.
[9] Zang Maolin (2018). Human Resource Management in the Era of Big Data. Journal of Human Resource and Sustainability Studies,p.25-27.
[10] Wang L (2018). Algorithm of continuous attribute discretization based on improved particle swarm. CEA, p.21-30.