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Time Optimization of Multiple Knowledge Transfers in the Big Data Environment

Chuanrong Wu1, *, Evgeniya Zapevalova1, Yingwu Chen2, Feng Li3

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, 410073, China.
School of Computer & Communication Engineering, Changsha University of Science & Technology, Changsha, 410114, China.

* Corresponding author: Chuanrong Wu. Email: .

Computers, Materials & Continua 2018, 54(3), 269-285.


In the big data environment, enterprises must constantly assimilate big data knowledge and private knowledge by multiple knowledge transfers to maintain their competitive advantage. The optimal time of knowledge transfer is one of the most important aspects to improve knowledge transfer efficiency. Based on the analysis of the complex characteristics of knowledge transfer in the big data environment, multiple knowledge transfers can be divided into two categories. One is the simultaneous transfer of various types of knowledge, and the other one is multiple knowledge transfers at different time points. Taking into consideration the influential factors, such as the knowledge type, knowledge structure, knowledge absorptive capacity, knowledge update rate, discount rate, market share, profit contributions of each type of knowledge, transfer costs, product life cycle and so on, time optimization models of multiple knowledge transfers in the big data environment are presented by maximizing the total discounted expected profits (DEPs) of an enterprise. Some simulation experiments have been performed to verify the validity of the models, and the models can help enterprises determine the optimal time of multiple knowledge transfer in the big data environment.


Cite This Article

C. Wu, E. Zapevalova, Y. Chen and F. Li, "Time optimization of multiple knowledge transfers in the big data environment," Computers, Materials & Continua, vol. 54, no.3, pp. 269–285, 2018.

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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