[1]黄承宁.大数据环境下分布式图计算算法的改进与应用[J].计算机技术与发展,2019,29(05):187-191.[doi:10. 3969 / j. issn. 1673-629X. 2019. 05. 039]
　HUANG Cheng-ning.Improvement and Application of GridGraph Algorithm in Big Data Environment[J].,2019,29(05):187-191.[doi:10. 3969 / j. issn. 1673-629X. 2019. 05. 039]
点击复制

大数据环境下分布式图计算算法的改进与应用()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年05期
页码:
187-191
栏目:
应用开发研究
出版日期:
2019-05-10

文章信息/Info

Title:
Improvement and Application of GridGraph Algorithm in Big Data Environment
文章编号:
1673-629X(2019)05-0187-05
作者:
黄承宁
南京工业大学浦江学院,江苏 南京 211222
Author(s):
HUANG Cheng-ning
Nanjing Tech University Pujiang Institute,Nanjing 211222,China
关键词:
大数据; GridGraph; 分布式计算; 图计算
Keywords:
big data; GridGraph; distributed computing; graph calculation
分类号:
TP39
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 05. 039
摘要:
图是一种非常重要的数据结构,能够充分描述自然界中各事物之间的联系和依赖属性,因此图在计算机领域中应用广泛。 很多诸如网络路由、网络流等问题都可以在图论的支撑下,借助相关算法得到高效解决。 随着 Web2. 0、大数据、社交网络、机器学习和数据挖掘等技术的高速发展,很多领域抽象出来的图规模呈指数级增长,图中的节点、边及权重爆发式地达到亿万级别,对图计算性能提出了新的要求。 文中从图计算框架理论基础 BSP 框架分析,剖析了目前的分布式图处理平台处理海量 Natural Graphs 的算法与性能,提出将图中边组织并组到一个“ grid冶 中展示和图分割模式的GridGraph 图计算系统。 实验结果表明,GridGraph 系统的图计算性能超越了单机图计算系统,甚至比需要更多资源的主流分布式图形处理系统更快。
Abstract:
The graph is a very important data structure, which can fully describe the relationship between things in nature and their dependency attributes. Therefore,graph is widely used in the field of computer. Many problems such as network routing and network flow can be efficiently solved by relevant algorithm under the support of graph theory. With the rapid development of technology such as Web2. 0, big data, social network, machine learning and data mining, the size of graphs abstracted from many fields increases exponentially,and the nodes,edges and weights of graphs explode to billions of levels,which puts forward new requirements for graph computing performance. Based on the BSP framework, we analyze the algorithm and performance of the current distributed graph processing platform when processing large scale Natural Graphs and put forward the idea of organizing and grouping the nodes and edge lines of a graph into two different grids to be displayed using GridGraph calculation system. The experiment shows that the performance of the GridGraph system go beyond the stand-alone graph computing system,even faster than the popular distributed graphics processing system which requires more resources.

相似文献/References:

[1]严霄凤,张德馨.大数据研究[J].计算机技术与发展,2013,(04):168.
　YAN Xiao-feng,ZHANG De-xin.Big Data Research[J].,2013,(05):168.
[2]王雷,陈彦先,袁哲,等. 面向预拌混凝土行业的云计算[J].计算机技术与发展,2014,24(08):14.
　WANG Lei,CHEN Yan-xian,YUAN Zhe JI Xu. Research on Cloud Computing for Ready-mixed Concrete Industry[J].,2014,24(05):14.
[3]金宗泽,冯亚丽,文必龙,等. 大数据分析流程框架的研究[J].计算机技术与发展,2014,24(08):117.
　JIN Zong-ze,FENG Ya-l,WEN Bi-long,et al. Research on Framework of Big Data Analytic Process[J].,2014,24(05):117.
[4]张也弛,周文钦,石润华. 一种面向云的大数据完整性检测协议[J].计算机技术与发展,2014,24(09):68.
　ZHANG Ye-chi,ZHOU Wen-qin,SHI Run-hua. A Big Data Integrity Checking Protocol for Cloud[J].,2014,24(05):68.
[5]谢怡,王航,刘新瀚,等. 大数据环境下数据读取关键技术研究[J].计算机技术与发展,2015,25(02):113.
　XIE Yi,WANG Hang,LIU Xin-han,et al. Research on Data Reading Techniques Based on Big Data Environment[J].,2015,25(05):113.
[6]付燕平,罗明宇,刘其军. 大数据三维模型快速显示技术研究[J].计算机技术与发展,2015,25(05):87.
　FU Yan-ping,LUO Ming-yu,LIU Qi-jun. Research on Fast Display Technology for Big Data Three-dimensional Model[J].,2015,25(05):87.
[7]赵震,任永昌. 大数据时代基于云计算的电子政务平台研究[J].计算机技术与发展,2015,25(10):145.
　ZHAO Zhen,REN Yong-chang. Research on E-government Platform Based on Cloud Computing in Big Data Era[J].,2015,25(05):145.
[8]胡存刚,程莹. 基于粒子群算法的大数据智能搜索引擎的研究[J].计算机技术与发展,2015,25(12):14.
　HU Cun-gang,CHENG Ying. Research on Big Data Intelligent Search Engine Based on PSO[J].,2015,25(05):14.
[9]孔钦,叶长青,孙赟.大数据下数据预处理方法研究[J].计算机技术与发展,2018,28(05):1.[doi:10．3969/j．issn．1673－629X．2018．05．001]
　KONG Qin,YE Changqing,SUN Yun.Research on Data Preprocessing Methods for Big Data[J].,2018,28(05):1.[doi:10．3969/j．issn．1673－629X．2018．05．001]
[10]杨明,李铁冰,姜茸,等.基于AHP 的大数据可用性及挖掘方案模型研究[J].计算机技术与发展,2018,28(05):51.[doi:10.3969/j.issn.1673－629X.2018.05.012]
　YANG Ming,LI Tie-bing,JIANG Rong,et al.Research on Model of Big Data Usability and Mining Strategy Based on AHP[J].,2018,28(05):51.[doi:10.3969/j.issn.1673－629X.2018.05.012]

更新日期/Last Update: 2019-05-10