Abstract
With the increasing development of big data analytic research, abundant big data analytic models have been the most important tools in many fields of social. Federal Education Grant program is especially important for development of universities all over the world. A reasonable investment for a university could provide students more intensive supports, which eventually resulted in increasing the ratio of talented persons and greater contributions to society. However, the study on the optimization of the investment proportion of education grant is rarely few, and there is even no further study on the integration of it in the field of big data analytics. According to it this article aims to use four different models to invest university selectively and determine an optimal investment ratio.
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Acknowledgement
This work is supported by Zhejiang Provincial Natural Sciences Foundation of China (Grant No. LQ14F020002) and project also supported by the National Training Foundation of Innovation and Entrepreneurship for Under-graduates(Grant No.201613021005).
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Li, W., Yang, J., Wu, W., Ci, W., He, J., Fu, L. (2016). A Multi-Model Based Approach for Big Data Analytics: The Case on Education Grant Distribution. In: Morishima, A., et al. Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9865. Springer, Cham. https://doi.org/10.1007/978-3-319-45835-9_2
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