@Article{cmes.2018.04041, AUTHOR = {Chuanrong Wu, Evgeniya Zapevalova, Yingwu Chen, Deming Zeng, Francis Liu}, TITLE = {Optimal Model of Continuous Knowledge Transfer in the Big Data Environment}, JOURNAL = {Computer Modeling in Engineering \& Sciences}, VOLUME = {116}, YEAR = {2018}, NUMBER = {1}, PAGES = {89--107}, URL = {http://www.techscience.com/CMES/v116n1/33877}, ISSN = {1526-1506}, 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.}, DOI = {10.31614/cmes.2018.04041} }