摘要
传统的分布式大数据检索系统构建的数据仓库模型不稳定,导致数据检索时间较长,因此设计基于Hadoop的分布式大数据检索系统。该系统硬件上采用SSD固态硬盘存储数据,软件上根据分布式大数据检索系统的特性设置实时数据更新模式扩宽检索范围,基于Hadoop构建数据仓库模型,采用数据聚类融合算法检索分布式大数据。实验结果表明,设计系统能够有效缩短检索时间,节约检索成本。
The data warehouse model constructed by the traditional distributed big data retrieval system is unstable,which leads to longer data retrieval time.Therefore,a distributed big data retrieval system based on Hadoop is designed.The hardware uses SSD solid-state hard drives to store data.Based on the characteristics of the distributed big data retrieval system,the software sets up a real-time data update mode,expands the scope of retrieval,builds a data warehouse model based on Hadoop,and uses data clustering fusion algorithms to retrieve distributed big data.The experimental results show that the design system can effectively shorten the retrieval time and save the retrieval time cost.
作者
赵雪琴
ZHAO Xueqin(School of Electronics and Information Engineering,Sias University,Zhengzhou Henan 451150,China)
出处
《信息与电脑》
2021年第13期141-143,共3页
Information & Computer
