OLAP organizes multidimensional data against data cube model, which is convenient to queries.
OLAP按照数据立方体模型组织多维数据，从而方便了查询。
According to the practical context, this thesis raises a kind of hybrid data cube model and its query and incremental update algorithm.
另外根据实际应用背景，提出了一种混合数据立方体模型，并给出了其相应的查询和增量更新算法。
The query model defines the concepts of dimension selections, edges, cube views, and the aggregations and manipulations of dimensional data.
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 returns data aggregated at a single slice of the cube model.
查询返回的数据聚合在多维数据集模型的一个切片上。
A cube model provides a new perspective for the information consumers from which to understand their data.
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。
SQL queries can be constructed to retrieve OLAP data based on either the cube model or cube objects.
可以根据多维数据集模型或多维数据集对象构造SQL查询来获得OLAP数据。
On the Dimensional Model page, you can add measures, dimensions, and dimension levels to the data cube.
在Dimensionalmodel页中，您可以向数据立方中添加度量、维度和维度级别。
To build the individual cube model according to the country and language, store localized data in database in different languages, as illustrated in Figure 8.
为了根据不同的国家和语言构建单个多维数据集模型，需要将本地化数据存储在不同语言的数据库中，如图8所示。
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.
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。
Most of the information required to generate queries against the data warehouse tables is stored in the cube model-related objects.
生成针对数据仓库表的查询所需的大多数信息存储在多维数据集模型相关对象中。
A cube model contains metadata objects that describe relationships within the data that resides in the base tables and also describes where pertinent data is located.
多维数据集模型包含元数据对象，这些对象描述基表数据中的关系以及相关数据位于什么地方。
A cube model is built to represent a data structure and relationship in an OLAP data mart.
多维数据集模型代表OLAP数据市场中的数据结构和关系。
Methods: Analyze the characteristics of operational data, from the perspective of data model and cube data to organize and show the data.
方法：对业务数据的特性进行分析，从数据模型和数据立方体的角度组织和展现数据。
Multidimensional data cube is the major data model of data warehouse and OLAP server, but implementation of Multidimensional data Cuboids lattice is really time-consuming and space-consuming task.
多维数据立方体是数据仓库和OLAP服务器的主要数据组织模型，多维数据立方体格的实现占用大量的时间和空间资源。
In this paper, we address this issue by proposing a model of a data-cube and an algebra to support OLAP operations on this cube.
本文提出了数据超立方体模型，并在代数表达方面进行了改进，使得其可以支持OLAP操作。
Data that resides in a cube. A Mining Model created from multidimensional data can be used to create a dimension and a Virtual cube.
驻留在多维数据集中的数据。用多维数据创建的挖掘模型可用于创建维度和虚拟多维数据集。
A Mining Model created from multidimensional data can be used to create a dimension and a Virtual Cube.
用多维数据创建的挖掘模型可用于创建维度和虚拟多维数据集。
Proposed a general framework and a model suitable for multimedia mining, including multi-dimensional data cube, mining engine, interactive mining interface.
分析了目前信息环境对多媒体挖掘技术提出的需求，提出了一种适合多媒体挖掘的系统框架、一般结构和挖掘过程；
Proposed a general framework and a model suitable for multimedia mining, including multi-dimensional data cube, mining engine, interactive mining interface.
分析了目前信息环境对多媒体挖掘技术提出的需求，提出了一种适合多媒体挖掘的系统框架、一般结构和挖掘过程；
应用推荐