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Optimal Model of Continuous Knowledge Transfer in the Big Data Environment

Chuanrong Wu1, *, Evgeniya Zapevalova1, Yingwu Chen2, Deming Zeng3, Francis Liu4
School of Economy and Management, Changsha University of Science & Technology, Changsha, 410114, P. R. China.
College of Information System and Management, National University of Defense Technology, Changsha, 10073, China.
School of Business Administration, Hunan University, Changsha, 410082, China.
LMBA, Universite de Bretagne-SUD, 56017 Vannes, France
* Corresponding Author: Chuanrong Wu. Email address: .

Computer Modeling in Engineering & Sciences 2018, 116(1), 89-107. https://doi.org/10.31614/cmes.2018.04041

Abstract

With market competition becoming fiercer, enterprises must update their products by constantly assimilating new big data knowledge and private knowledge to maintain their market shares at different time points in the big data environment. Typically, there is mutual influence between each knowledge transfer if the time interval is not too long. It is necessary to study the problem of continuous knowledge transfer in the big data environment. Based on research on one-time knowledge transfer, a model of continuous knowledge transfer is presented, which can consider the interaction between knowledge transfer and determine the optimal knowledge transfer time at different time points in the big data environment. Simulation experiments were performed by adjusting several parameters. The experimental results verified the model’s validity and facilitated conclusions regarding their practical application values. The experimental results can provide more effective decisions for enterprises that must carry out continuous knowledge transfer in the big data environment.

Keywords

Big data, knowledge transfer, optimization model, simulation experiment, different time points.

Cite This Article

Wu, C., Zapevalova, E., Chen, Y., Zeng, D., Liu, F. (2018). Optimal Model of Continuous Knowledge Transfer in the Big Data Environment. CMES-Computer Modeling in Engineering & Sciences, 116(1), 89–107.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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