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Technology of Network & Communication
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 3
896-898,902

Network big data knowledge extension algorithm based on variable granularity and opportunistic scheduling

Huang Jinguo1
Liu Tao1
Zhou Xianchun2
Yan Xijun3
1. School of Information & Mechanical and Electrical Engineering, Jiangsu Open University, Nanjing 210017, China
2. School of Electronic & Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
3. College of Computer & Information, Hohai University, Nanjing 210098, China
DOI: 10.19734/j.issn.1001-3695.2017.09.0947

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, 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.

Foundation Support

国家自然科学基金资助项目(11202106,61201444)
江苏省高校自然科学研究面上基金资助项目(15KJD520003)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2017.09.0947
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 3
Section: Technology of Network & Communication
Pages: 896-898,902
Serial Number: 1001-3695(2019)03-050-0896-03

Publish History

[2019-03-05] Printed Article

Cite This Article

黄金国, 刘涛, 周先春, 等. 基于可变粒度机会调度的网络大数据知识扩充算法 [J]. 计算机应用研究, 2019, 36 (3): 896-898,902. (Huang Jinguo, Liu Tao, Zhou Xianchun, et al. Network big data knowledge extension algorithm based on variable granularity and opportunistic scheduling [J]. Application Research of Computers, 2019, 36 (3): 896-898,902. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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