摘要
对大数据库中信息传输效率进行管理,能够有效提高数据库的信息管理质量。对信息传输快速性管理,需要计算个信息属性权值,给出信息间关联度判断阈值,完成信息传输快速性管理。传统方法首先计算信息关联决策,获取信息间的关联度,但忽略了给出关联度判断阈值,导致管理效果不理想。提出大数据库中信息传输快速性管理方法,先基于信息熵的懒散关联分类思想,计算各信息属性值,得到各属性值类分布状态,对信息熵最小的k个属性值进行选取,针对少量信息样本,利用机器学习方法,将信息传输训练集投影到K个属性值,计算各信息属性权值,给出信息间关联度判断阈值,完成大数据库中信息传输快速性的管理。仿真证明,所提方法有效地提升数据库的信息管理质量。
The traditional method calculates the information association decision and obtains the correlation degree between information, but ignores the relevance threshold, which leads to the unsatisfactory management effect. The management method of rapid information transmission in large database is proposed. First of all, on the basis of lazy associative classification of information entropy, the information attribute values are calculated to obtain distribution state of each attribute value. The K attribute values with the minimum information entropy are chosen. For a small a- mount of information samples, the machine learning method is used to project the information transmission training on the K attribute values. Each weight of information attribute is calculated and judging threshold of correlation between information is given. Thus, the management of rapidity of information transmission in large database is completed. Simulation results show that the proposed method can effectively improve the information management quality of database.
作者
曾劲松
饶云波
ZENG Jin - song;RAO Yun - bo(Southwestern University of Finance and Economics, Chengdu Sichuan 610074, China;School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu Sichuan 610054, China)
出处
《计算机仿真》
北大核心
2018年第4期118-121,共4页
Computer Simulation
基金
中国金融信息港创新研究中心(JBK150401)
关键词
大数据库
信息传输
快速性
管理
Large database
Information transmission
Rapidity
Management
