多维模型：多维模型用于业务分析。
Dimensional model: The dimensional model is used for business analytics.
文中还简要地给出多维模型。
对于业务分析系统，该公司需要基于规范化的数据模型创建多维模型。
For the business analysis system, the company needs to create multidimensional models based on the normalized data model.
选择您的标准化多维模型中的所有实体，并将这些实体拖放到图表编辑器中。
Select all the entities in your normalized dimensional model, and drag them and drop them into the Diagram editor.
JohnDonaldson分享了关注于测试角色和测试类型的多维模型。
John Donaldson Shared a multi-dimensional model of tests that look at test roles and test types.
同时详细探讨了通信人才和通信设备两个数据集市及多维模型的设计；
At the same time, it discusses two data marts , communication personal and equipment, and it also discuses the design details of multidimensional schema.
下面我们将探讨这一多维模型，并介绍在实践中如何使用可测量和可量化的变量来构造它。
Below we explore this multidimensional model and how you construct it, in practical terms, with variables that are measurable and quantifiable.
本文指出组成压阻型压力传感器电桥的电阻受温度、输入电压、压力的共同影响，在此基础上提出非线性扩散电阻的多维模型。
The phenomenon is called electric nonlinearity for sensitivity. The nonlinear resistors, forming the bridge in pressure sensor, are influenced by temperature, input voltage and pressure together.
创建非标准化多维逻辑模型的过程现在已经完成。
The process of creating a de-normalized dimensional logical model is now complete.
谓词可以应用于多维数据集模型中维的任何属性。
Predicates can be applied to any attribute of a dimension in a cube model.
在本小节，我们继续将非标准化多维逻辑数据模型转换为多维物理数据模型。
In this section, we are going to transform the de-normalized dimensional logical data model to dimensional physical data model.
在上面的小节中，讨论了如何从非标准化多维逻辑数据模型转换为一个有效的多维物理数据模型。
In the section above, one valid dimensional physical data model is transformed from the de-normalized dimensional logical data model.
多维数据集模型可以看作星形联结或雪花形联结模式的抽象。
The cube model can be seen as an abstraction of a star-join or snowflake-join schema.
该文件与导出多维数据集模型时创建的文件不同且单独存在。
This file is separate and different from the file that is created when you export your cube model.
多维数据集模型代表OLAP数据市场中的数据结构和关系。
A cube model is built to represent a data structure and relationship in an OLAP data mart.
将多维逻辑数据模型转换为多维物理数据模型
Transforming the dimensional logical data model to dimensional physical data model
将非标准化多维逻辑数据模型转换为多维物理数据模型。
Transform de-normalized dimensional logical data model to dimensional physical data model.
多维数据集模型包含一组(dimension,join)引用。
The cube model contains a set of (dimension, join) references.
在Cubing模型中，大多数OLAP对象是从InfoSphereDataArchitect中的多维物理数据模型生成的，比如多维数据集模型、事实、维度、度量、分层结构和级别。
In the Cubing model, most OLAP objects are generated from the dimensional physical data model in InfoSphere data Architect, such as cube models, facts, dimensions, measures, hierarchies, and levels.
这些对象引用多维数据集模型中使用的事实、维、层次结构和级对象，它们与多维数据集模型中的这些对象是相似的。
These objects reference the facts, dimension, hierarchy, and level objects that are used in the cube model, and they are similar to these objects in the cube model.
安全模型并不包含这些OLAP对象的实际定义 —那是多维数据集模型的工作。
The security model does not contain the actual definition of these OLAP objects—the cube model does.
现在我们已经将通过多维符号生成的多维物理数据模型添加到源多维逻辑数据模型中。
Now we have the dimensional physical data model generated with the dimensional notations added to the source dimensional logical data model.
但是，您应该掌握如何配置多维数据集模型使之支持全球化特性。
But you should understand how to configure the cube model to support the globalization feature.
一个切片包含来自多维数据集模型的每个维的层次结构的一个级。
A slice includes a level from a hierarchy of each dimension of the cube model.
来自查询访问的多维数据集模型切片的属性。
Attributes from the slice of the cube model accessed by the query.
由于模型文件已经进行正确的配置，您就不必再更新多维数据集模型了。
You don't need to update the cube model since the model file has been configured correctly.
查询返回的数据聚合在多维数据集模型的一个切片上。
The query returns data aggregated at a single slice of the cube model.
本文假设多维数据集模型的维具有平衡的层次结构和标准部署。
This paper assumes the dimensions of the cube model have hierarchies of balanced type and standard deployment.
可以使用多维数据集对多维数据集模型进行优化，使它更适应最活跃、最重要的多维数据集模型区域。
Cubes can be used when optimizing a cube model to specify the regions of the cube model that are the most active and the most important.
多维数据集模型让信息消费者能够从一个新的视角理解数据。
A cube model provides a new perspective for the information consumers from which to understand their data.
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