在设计维度表时，需要计划变更维度数据。
As you're designing your dimension table, you need to plan for changing dimensional data.
设计维度数据仓库是一个复杂主题，这三篇文章只触及到它的皮毛。
Designing a dimensional data warehouse is a complex subject, and I have just touched the surface of it in these three articles.
如果其目标是执行多维数据分析，那么维度数据模型就是这里的惟一选择。
If the objective is to perform multidimensional data analysis, a dimensional data model would be the only choice here.
查询模型定义维度选择、边界、立方体视图以及维度数据的聚合与操纵等概念。
The query model defines the concepts of dimension selections, edges, cube views, and the aggregations and manipulations of dimensional data.
维度数据模型也可能是较好的选择，因为它是用户友好的，并具有更好的性能。
A dimensional data model might also be a good choice because it is user-friendly and has better performance.
点和区间混合型维度数据集是空间数据库系统和GIS中重要的数据对象。
Data sets of point and interval dimensions are important in spatial database system and GIS.
它还可以用于减少数据集中的维度数据，以便只专注于最有用的属性，或者用于探明趋势。
It also can be used to reduce the number of dimensions in a data set in order to focus on only the most useful attributes, or to detect trends.
这种方法只能用于维度数据源，如olap数据或维度建模关系型(DMR)数据源。
This method applies to dimensional data sources only such as OLAP sources or dimensionally modelled relational (DMR) sources.
数据保存在架构中心的单个事实数据表中而其他维度数据存储在维度表中的一种关系数据库结构。
A relational database structure in which data is maintained in a single fact table at the center of the schema with additional dimension data stored in dimension tables.
因为本文没有包括ER建模，所以本小节将讨论维度数据建模，这是数据集市设计中最重要的数据建模方法。
Since ER modeling is not included in this article, this section will discuss dimensional data modeling, which is the most important data modeling methodology in data mart design.
整个Section4都关于设计维度数据库，该手册很好地概述了维度数据建模和使用Informix实现一个维度数据库设计。
The entire Section 4 is on designing dimensional databases, and the manual offers a very good overview of dimensional data modeling and implementing a dimensional database design with Informix.
ROLAP数据立方体是按关系表格的集合实现的(最多可达维度数目的两倍)，来代替多维阵列。
The ROLAP data cube is implemented as a collection of relational tables (up to twice as many as the number of dimensions) instead of as a multidimensional array.
随着维度数目的增加，立方体变得更稀疏，即表示某些属性组合的多个单元是空的，没有集合的数据。
As the number of dimensions increases, the cube becomes sparser-that is, many cells representing specific attribute combinations are empty, containing no aggregated data.
随着维度数目的增加，立方体变得更稀疏，即表示某些属性组合的多个单元是空的，没有集合的数据。
As the number of dimensions increases, the cube becomes sparser-that is, many cells representing specific attribute combinations are empty, containing no aggregated data.
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