摘要
针对当前大型软件系统因支撑数据的质量问题给整个系统带来的直接或潜在危害,深入分析了产生数据质量问题的根本原因、易发阶段、严重等级等相关要素,剖析了科学的数据质量管理制度和完备的数据质量管理体系,提出了数据采集、数据整合、数据清洗、数据挖掘等先进的数据质量控制工作原理。以基于分类模式的数据挖掘技术为例,介绍实现数据质量控制的典型方法,从而使得大型软件系统在研制和维护过程中可通过所研究的标准、体系和方法基本形成适用于各领域系统数据质量问题的高效预防措施和最佳解决方案。
Considing to the direct or potential disadvantage on the current large-scale software systems because of the data-quality prob-lems,basic causes,prone stage,severity rating and other related elements of the data-quality problems are analyzed deeply,it amplifies scientific data-quality management regulations,perfect data quality management architecture,and puts forward advanced control theories,such as data gathering,data integration,data cleaning,data minning,etc,then introduces the typical method of data quality control illustrated by the case of data minning technique based on classification mode.Thus in the development and maintenance process of large-scale software system basically can form the efficient prevention measures and best solution applied to data-quality problems in all fields by the study of standards,architecture and methods.
出处
《计算机工程与设计》
CSCD
北大核心
2011年第3期963-967,987,共6页
Computer Engineering and Design
关键词
软件系统
数据质量
管理体系
数据分类
数据挖掘
控制方法
software system
data quality
management architecture
data classification
data minning
control methods
