[1]王晓妮,段群,韩建刚.基于云计算的数据挖掘系统设计与实现[J].计算机技术与发展,2019,29(03):178-182.[doi:10.3969/ j. issn.1673-629X.2019.03.037]
　WANG Xiao-ni,DUAN Qun,HAN Jian-gang.Design and Implementation of Data Mining System Based on Cloud Computing[J].,2019,29(03):178-182.[doi:10.3969/ j. issn.1673-629X.2019.03.037]
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基于云计算的数据挖掘系统设计与实现()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年03期
页码:
178-182
栏目:
应用开发研究
出版日期:
2019-03-10

文章信息/Info

Title:
Design and Implementation of Data Mining System Based on Cloud Computing
文章编号:
1673-629X(2019)03-0178-05
作者:
王晓妮1; 段群2; 韩建刚3
1. 咸阳师范学院 信息中心,陕西 咸阳 712000;2. 咸阳师范学院 计算机学院,陕西 咸阳 712000;3. 西北机电工程研究所 生产部电调室,陕西 咸阳 712000
Author(s):
WANG Xiao-ni1; DUAN Qun2; HAN Jian-gang3
1. Information Center,Xianyang Normal University,Xianyang 712000,China;2. School of Computer Science,Xianyang Normal University,Xianyang 712000,China;3. Electric Control Room of Production Department,Northwest Electrical and Mechanical Engineering Resea
关键词:
云计算; 数据挖掘; 海量数据; Map / Reduce
Keywords:
cloud computing; data mining; massive data; Map / Reduce
分类号:
TP302
DOI:
10.3969/ j. issn.1673-629X.2019.03.037
摘要:
为了解决数据出现指数式增长所导致的海量数据与传统数据挖掘系统计算能力有限的矛盾日益尖锐这个问题,提出了一种将云计算技术和数据挖掘有机结合的解决方案。 通过采用 Map / Reduce 这种能够处理大量半结构化数据集合的并行编程模型方法,将云计算技术融入海量数据挖掘过程中,设计并实现了基于云计算的数据挖掘系统。 通过对高校师生在图书馆的电子文献资料查阅日志数据集的挖掘分析,对该系统的性能进行了测试,表明该系统能够实现根据用户需求为其提供即时服务。 实验结果表明,该系统的运行效率和挖掘速度均高于单机系统,而且随着数据量的增加,挖掘效率的优势愈发明显。 故该系统能够满足用户需求,可以有效解决传统数据挖掘系统中的技术瓶颈。
Abstract:
In order to solve the problem of the ever-increasing contradiction between the massive data and the limited computing capacity of traditional data mining system caused by the exponential growth of data,we propose a solution combined cloud computing technologyand data mining organic. By using Map / Reduce,a parallel programming model method that can handle a large number of semi-structured data collections,cloud computing technology is integrated into massive data mining process,and a cloud-based data mining system is designed and implemented. This system is tested by excavating and analyzing log datasets of university educators and students in library e-documents. The results prove that the system can provide even services for users according to their needs. The experiment shows that the running efficiency and speed of the system are higher than that of the single machine system,and with the increase of data volume,the advantage of mining efficiency is more obvious. Therefore,the system can meet users’ needs and effectively solve the technical bottleneck of traditional data mining systems.

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更新日期/Last Update: 2019-03-10