基于可变粒度机会调度的网络大数据知识扩充算法.

In order to meet the needs of the network under the background of big data, and eliminate inferior data interference data knowledge high precision requirements of large data transmission, this paper proposed variable size adjustment scheme based on the algorithm to expand the network of large data knowledge opportunistic scheduling. Based on the analysis of large data network characteristics, it normalized the adaptive vector encoding, capture the heterogeneous characteristics of large data network, using multi order back-propagation network of heterogeneous data, and then through the real-time transmission of large data network to achieve opportunistic scheduling. At the same time, the knowledge engineering system composed of network data segmentation of fine-grained big data based on the multidimensional feature dimension, the granularity of knowledge transformation was known, then adjusted the size of the dynamic characteristics, making big data set of knowledge engineering with linear characteristics and clear geometric characteristics, improved the accuracy of knowledge acquisition through knowledge expansion. The experimental results are compared with the algorithm based on fine grained knowledge acquisition, which proves the high reliability, real time and high efficiency of network data transmission. [ABSTRACT FROM AUTHOR]