提出了扩展多维数据模型。
研究了多维数据模型的设计模式，给出了三种主要的设计方法。
Based on the study of designing pattern of multi-dimensional data model, three designation methods are pointed out.
本文介绍了一种基于关系数据库、利用XML技术实现多维数据模型方法。
This paper presented a method of construction of multi-dimensional data model based on relation databases and XML technology.
文中根据多维数据模型的结构特点以及OLAP需求提出了一种变粒度存储策略。
According to the structural characteristic of multidimensional data model and OLAP's needs, a various granularity storage method has been presented to store historical data.
论文最后对全文进行了总结，并对多维数据模型及多维计算的研究方向进行了展望。
In the end, research results of the dissertation are summarized, after that, some orientations of multidimensional data model and multidimensional computation for further research are also provided.
建立数据模型是构造数据仓库的重要步骤之一，多维数据模型是数据仓库设计中广泛采用的概念模型。
Constructing data model is one of important steps for building data Warehouse. Multidimensional model is a conceptual model widely used in data Warehouse designing.
本文介绍了ER关系模型和多维数据模型的一些基本概念，着重介绍了需求管理的多维数据模型设计。
Some basic concepts of E-R relation model and multi-dimension data model are introduced, and design of multi-dimension data model of demand management is described in detail.
我们已经完成了使用InfoSphereDataArchitect V7.5.3并通过正向工程创建多维数据模型的工作流程。
We have completed the workflow to create multidimensional data models through forward engineering using InfoSphere data Architect V7.5.3.
其中重点介绍了多维数据模型的维表、事实表的结构设计，分析了数据模型的构建、数据抽取工具和数据维护工具的设计及实现。
Specially, the paper presented the creating of dimension table, fact table of multi-dimension data model, analyzed the construction of data model, data extraction and data maintenance tools.
该文在对传统的多维数据模型进行综合分析的基础之上，利用了包括空间概念的层次分类结构、空间多维向量等工具来表示模型。
This paper based the analysis of the traditional multi-dimensional data model, used the tools include arrangement structure in space, multi-dimensional in space to express the model.
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.
在上面的小节中，我们已经请利益相关者审核了非标准化多维逻辑数据模型。
In the section above, we have the de-normalized dimensional logical data model reviewed by stakeholders.
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.
基于规范化数据模型发现多维信息。
Discovering multidimensional information based on a normalized data model.
右键单击非标准化多维逻辑数据模型节点，然后在上下文菜单中单击TransformtoPhysicalDataModel。
Right click the de-normalized dimensional logical data model node, and click Transform to Physical data model from the context menu.
分析转换后的多维物理数据模型。
在InfoSphereDataArchitectV7.5.3中添加了七个规则，均可用于多维物理数据模型验证。
Seven rules are added in InfoSphere data Architect V7.5.3 for dimensional physical data model validation.
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.
您可以将更多特定于数据库的信息添加到多维物理数据模型，但是我们此处不做过多介绍。
You can add more database-specific information to the dimensional physical data model, but we will not introduce much here.
为了确保转换后的多维物理数据模型符合企业标准，总是建议您对模型进行分析。
To make sure the transformed dimensional physical data model is compliant with enterprise standards, analyzing the model is always recommended.
启用多维符号的第一步是在逻辑数据模型中实现多维功能。
The first step for enabling dimensional notation is to enable dimensional capability in the logical data 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.
用于创建物理数据模型的图形化工具，DB 2基于SQL的数据仓库结构数据流，以及olap多维数据集模型。
Graphical tool for creating physical data models, DB2 SQL-based warehouse construction data flows, and OLAP cube models.
现在用户可以生成ddl，在以后将它用于多维模式部署，并从多维物理数据模型执行以下操作。
Now the user can generate DDL, which can be used for the dimensional schema deployment later, from the dimensional physical data model.
将多维物理数据模型转换为Cubing或Cognos模型。
Transforming the dimensional physical data model to a Cubing or Cognos model.
将规范化逻辑数据模型转换为非规范化多维逻辑数据模型。
Transforming the normalized logical data model to a de-normalized dimensional logical data model.
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.
有几个因素会对性能产生负面影响，如数据模型的设计不正确（例如，在您的数据模型中产生对多维数组的需求）。
There are several factors that adversely affect performance, such as improper design of the data model (for example, creating a need for multidimensional arrays in your data model).
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
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