基于关系模型的分布式数据库增量更新方法
Incremental Updating Method of Distributed Database Based on Relational Model
孙滨 1冯乃勤2
作者信息
- 1. 郑州工业应用技术学院信息工程学院,河南 郑州 451150
- 2. 河南师范大学计算机与信息工程学院,河南 新乡 453007
- 折叠
摘要
数据库更新过程中易受到噪声数据的干扰,影响分布式数据库的数据更新与查询性能,为此提出基于关系模型的分布式数据库增量更新方法.利用数学形态学与滤波器分离出分布式数据库中存在的低频信号,通过K-SVD字典处理噪声信号,完成分布式数据库数据的降噪处理.在关系模型的基础上通过边界学习算法离散化处理分布式数据库中存在的数据,并根据定性基准对数据分组,结合IUBM算法挖掘数据之间的关联规则,以此检测数据库增量.以新鲜度为依据,排序分布式数据库中存在的数据,完成分布式数据库的增量更新.实验结果表明,所提方法的去噪效果好,更新效率高,更新后数据库完整度更高.
Abstract
During the update process,the database is susceptible to interference from noisy data,which affects the data update and query performance of distributed database.Therefore,an incremental updating method of distributed database based on relational model was proposed.Firstly,low-frequency signals in the distributed database were sepa-rated by mathematical morphology and filter.And then,noise signals were processed by K-SVD dictionary,so that the noise reduction of data in distributed database was completed.Based on the relational model,the boundary learning algorithm was adopted to discretize the data existing in the distributed database.Meanwhile,the data were grouped ac-cording to the qualitative criterion.Moreover,IUBM algorithm was used to mine the association rule between data,thus detecting the database increment.Based on freshness,the data in the distributed database were ranked.Finally,the incremental update was finished.The experimental results show that the proposed method has good denoising effect,high updating efficiency and higher database integrity.
关键词
关系模型/数学形态滤波/关系模型/数据库增量更新Key words
Relational model/Mathematical morphological filtering/Incremental update of database引用本文复制引用
基金项目
河南省科技厅科技攻关支持项目(222102210159)
河南省科技厅科技攻关支持项目(202102210361)
河南省科技厅软科学支持项目(222400410228)
河南省教育厅高等学校青年骨干教师培养资助项目(2019)(2019GGJS279)
教育部高教司产学合作协同育人资助项目(2021)(202102633007)
出版年
2024
NETL
NSTL
万方数据