基于可变粒度机会调度的网络大数据知识扩充算法

Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-05-18 Cooperative journals: 《计算机应用研究》

黄金国 刘涛 周先春 严锡君

Abstract： 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, variable size adjustment scheme based on the algorithm to expand the network of large data knowledge opportunistic scheduling is proposed. Based on the analysis of large data network characteristics, the adaptive vector encoding, capture the heterogeneous characteristics of large data network, using multi order back-propagation network of heterogeneous data is normalized, 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 is known, then adjust the size of the dynamic characteristics, making big data set of knowledge engineering with linear characteristics and clear geometric characteristics, improve 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.

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