Research and analysis for real-time streaming big data based on controllable clustering and edge computing algorithm

doi:10.1109/ACCESS.2019.2955992

	Research and analysis for real-time streaming big data based on controllable clustering and edge computing algorithm
	Li, Xiang1 ; Zhang, Zijia 2
	2019
发表期刊	IEEE Access
ISSN	2169-3536
卷号	7 页码:171621-171632
摘要	Aiming at the low efficiency, poor performance and weak stability of traditional clustering algorithms and the poor response to the processing of massive data in real time, a real-time streaming controllable clustering edge computing algorithm (SCCEC) is proposed. First, the data tuples that arrive in real time are pre-processed by coarse clustering, the number of clusters, and the position of the center point are determined, and a set formed by macro clusters having differences is formed. Secondly, the macro cluster set obtained by the coarse clustering is sampled, and then K-means parallel clustering is performed with the largest and smallest distances, thereby realizing fine clustering of data. Finally, the completely clustering algorithm and the edge-computing algorithm are combined to realize the clustering analysis under the edge-computing framework. The experimental results show that the proposed algorithm has the advantages of high efficiency, good quality, and strong stability. It can quickly obtain the global optimal solution, and deal with massive data with high real-time performance. It can be used for real-time streaming data aggregation under big data background. © 2013 IEEE.
关键词	Algorithms Big data Cluster analysis Edge computing Efficiency Metadata Random access storage Real time systems Clustering Computing algorithms Computing frameworks Global optimal solutions Real time performance Real time streaming Research and analysis Traditional clustering
DOI	10.1109/ACCESS.2019.2955992
收录类别	EI ; SCIE
语种	英语
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000509374200022
出版者	Institute of Electrical and Electronics Engineers Inc., United States
EI入藏号	20200408087669
EI分类号	722.1 Data Storage, Equipment and Techniques ; 722.4 Digital Computers and Systems ; 723 Computer Software, Data Handling and Applications ; 723.2 Data Processing and Image Processing ; 913.1 Production Engineering
原始文献类型	Journal article (JA)
出版地	PISCATAWAY
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.cqcet.edu.cn/handle/39TD4454/3098
专题	人工智能与大数据学院
作者单位	1.School of Artificial Intelligence and Big Data, Chongqing College of Electronic Engineering, Chongqing; 401331, China; 2.School of Automation, Nanjing University of Information Science and Technology, Nanjing; 210044, China
第一作者单位	重庆电子科技职业大学
推荐引用方式 GB/T 7714	Li, Xiang,Zhang, Zijia. Research and analysis for real-time streaming big data based on controllable clustering and edge computing algorithm[J]. IEEE Access,2019,7:171621-171632.
APA	Li, Xiang,&Zhang, Zijia.(2019).Research and analysis for real-time streaming big data based on controllable clustering and edge computing algorithm.IEEE Access,7,171621-171632.
MLA	Li, Xiang,et al."Research and analysis for real-time streaming big data based on controllable clustering and edge computing algorithm".IEEE Access 7(2019):171621-171632.

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