摘要
数据仓库中的事实数据一般以最小粒度存储。而大量的细粒度数据具有很大的随机性,很少直接进行分析和处理,往往被聚集到一定层次的粗粒度数据。另一方面若采用ROLAP存储数据,则大量的细粒度数据将会影响查询的效率。本文介绍了一种基于时间维层次查询频率的粒度调整模型,它能根据用户在时间维层次的查询频率实现对数据粒度的调整。
The fact data in the data warehouse is generally stored in the minimum granularity.But A large number of fine-granularity data have great randomness, is seldom used to analysis and process directly.We are often gathered the data to a certain level of coarse-granularity data. On the other hand, if the data stored by the ROLAP, a lot of fine-granularity data will affect the efficiency of the query. This article describes a granularity adjustment model based on the time dimension hierarchy query frequency.This model can adjust the data granularity by the time dimension hierarchy query frequency.
关键词
粒度模型
数据仓库
时间维
granularity model
data warehouse
time dimension
