The OLAP data cube is best suited to reports that provide aggregated information, such as the number of work items that meet a set of criteria.
olap多维数据集最适合于提供聚合信息的报表，例如满足一组条件的工作项数。
If you want to create reports that show trends over time, such as burn-down or progress charts, you can most easily create them from the OLAP data cube.
若要创建显示一段时间内的趋势的报表(如减弱图表或进度图表)，则通过olap多维数据集可以最轻松地创建此类报表。
Graphical tool for creating physical data models, DB2 SQL-based warehouse construction data flows, and OLAP cube models.
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。
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。
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。
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。
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。
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。
Since data cubes are such a useful interpretation tool, most OLAP products are built around a structure in which the cube is modeled as a multidimensional array.
由于数据立方体是一个非常有用的解释工具，所以大多数OLAP产品都围绕着按多维阵列建立立方模型这样一个结构编制。
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。
A smart OLAP cube will realize when you should use an aggregate and re-write your query to use the aggregated data instead.
一个聪明的OLAP多维数据集将实现时，你应该使用一个聚合和重写你的查询，而不是使用汇总数据。
The book also integrates the latest technology information, such as data warehousing, OLAP, data mining, Mediator, data cube system were introduced.
书中还对信息集成的最新技术，例如数据仓库、OLAP、数据挖掘、Mediator、数据立方体系统等进行了介绍。
The techniques of the Data Warehousing includes the system structure of the data organization, the data collecting, data extraction, data loading, data cube and OLAP query technique.
该数据仓库建立技术包括数据组织体系结构、数据采集、抽取、加载、数据立方体及OLAP功能查询技术。
The techniques of the Data Warehousing includes the system structure of the data organization, the data collecting, data extraction, data loading, data cube and OLAP query technique.
该数据仓库建立技术包括数据组织体系结构、数据采集、抽取、加载、数据立方体及OLAP功能查询技术。
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