摘要
常规的城市交通大数据聚类挖掘系统数据库多为定向结构,数据存储及提取效率较低,导致挖掘FRFT幅度值下降,系统运行不稳定。因此,文章提出对基于MapReduce的城市交通大数据聚类挖掘系统的设计与分析。构建传感信息采集装置,接入复位挖掘控制电路,完成系统硬件的设计;建立交通MapReduce聚类挖掘协议栈,设计自适应模式,强化挖掘效果,进行自适应MapReduce聚类挖掘数据库的关联,完成系统软件的设计。测试结果表明,经过4次聚类挖掘,最终得出的RFRT幅度较好地被控制在4以下,在对城市交通大数据聚类挖掘时,挖掘稳定性更高,整体的处理效果更佳,具有实际的应用价值。
Conventional urban traffic big data clustering mining system databases are mostly directional structures,and the data storage and extraction efficiency are slow,resulting in a decrease in the mining FRFT amplitude value and unstable system operation.Therefore,this paper proposes the design and analysis of an urban traffic big data clustering mining system based on MapReduce.Build a sensing information acquisition device,connect it to the reset mining control circuit,and complete the design of the system hardware,establish a traffic MapReduce cluster mining protocol stack,design an adaptive mode,strengthen the mining effect,and carry out the association of the adaptive MapReduce cluster mining database to complete the design of the system software.The test results show that after 4 times of cluster mining,the final RFRT amplitude is well controlled below 4.When clustering and mining urban traffic big data,the mining stability is higher,the overall processing effect is better,and it has practical application value.
作者
孙玉坤
韩聿彪
SUN Yukun;HAN Yubiao(China Chemical Transportation Construction Group Operation Management(Shandong)Co,Ltd.,Jinan 250014,China;Shandong Institute of Information Technology Industry Development,Jinan 250014,China)
出处
《计算机应用文摘》
2023年第11期128-130,133,共4页
Chinese Journal of Computer Application
