摘要
在进行数据仓库的OLAP联机分析处理时,通常采用预先聚集(Aggregate)操作生成概括数据的方法提高查询效率;但是,基于星型模型的数据仓库中的维表的纠错改变和自然改变将会引发概括数据的不正确问题.本文通过研究星型模型维表及其变化的特点,提出了一种增量更新算法,在几乎不增加空间的情况下,降低时间复杂度的增量更新;并通过性能验证,分析算法的可行性.
In dealing with on-line analytical processing, in order to improve the querying efficiency, people often pre-aggregate the data in the data warehouse. But the corrective and natural changing of dimensional tables based on star schema make the aggregate data false. In this article, I advance an incrementing update algorithm by studying dimensional tables of star schema and their changing characteristic. This algorithm reduce the time cost and increase the space cost hardly. Finally,this article analyze the algorithm's performance by validating its feasibility.
出处
《厦门大学学报（自然科学版）》
CAS
CSCD
北大核心
2004年第2期170-174,共5页
Journal of Xiamen University：Natural Science
关键词
星型模型
增量更新算法
数据仓库
聚集纠错
维表
data warehouse
star schema
dimensional tables
aggregate correct
incrementing update
