The dynamic Crosstab and data cube feature.
动态交叉表和数据立方体特性。
Dynamic cross table and data cube elements.
动态交叉表及数据立方体(data cube)元素。
Thus we normally don't perform calculations within a data cube.
因此，通常不需要在数据立方体中进行计算。
The computation of data CUBE is necessary but high cost in data warehouse.
数据立方的计算在数据仓库中是非常必要但代价很大的操作。
This also means that we're not looking at real-time, dynamic data in a data cube.
这也意味着我们看到数据立方体中的数据并不是实时的，动态的数据。
Data cube computation is a well-known expensive operation and has been studied extensively.
数据立方计算是代价非常大的操作，并且被广泛研究。
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。
It is becoming one of the research focuses to compute data CUBE efficiency in data warehouse.
有效的数据立方计算成为研究的热点之一。
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。
Chapter 2 of data warehouse technology and the concept of a data cube is the theoretical foundation.
第2章介绍了数据仓库技术的概念并给出了数据立方体的理论基础。
At last, based on a mathematical tool: algebraic system, this paper gives definition of spatial data cube.
最后，基于数学工具———代数系统，给出了空间数据立方体严格的数学定义。
The Cross Tab element is also new for BIRT 2.2 and can be used to display data contained within a BIRT data cube.
跨标签元素也是BIRT2.2中新出现的元素，用来显示BIRT数据体中的数据。
Often selected data from a data mart is fed into a smaller database called a data cube for intensive processing.
从数据集市中经常选用的数据送入更小的数据库(叫数据体)，进行密集处理。
However, the huge size of data cube introduces a series of problems with respect to its computation and storage.
然而数据立方的巨大尺寸却给数据立方的计算和存储带来诸多难题。
And in addition to consuming data from data sets, charts can now use data from an existing report item or a data cube.
并且除了使用数据集中的数据之外，图表现在还可以使用来自现有报表项或者数据集的数据。
It's important to keep in mind that the data in a data cube has already been processed and aggregated into cube form.
记住这点是很重要的：数据立方体中的数据是已经过处理并聚合成立方形式。
Data cube compressed storage is an important research content in large-scale database about data cube optimization calculation.
数据立方体的压缩存储是大型数据库中立方体优化计算的重要研究内容。
To solve these problems from the root, it is exigent to explore efficient data cube computation methods and cube storage structures.
为了从根本上解决这些问题，需要探索有效的数据立方计算和组织方法。
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 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。
This format is likely to be efficient for large data collections, since the tables must include only data cube cells that actually contain data.
这种形式对大量的数据集合可能是有效的，因为这些表格必须只能包含实际有数据的数据立方单元。
A data cube system design based on queries was developed to quickly and effectively answer these queries within the system resource limitations.
为了在系统资源有限的情况下快速有效的回答这些查询，该文提出了基于查询的数据方体系统设计问题。
That the mining of cube gradient is integrated into online analytical processing also accords with users' interest arising when browsing the data cube.
将梯度挖掘与联机分析处理集成，也符合用户在浏览数据立方时产生的挖掘兴趣。
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。
Size estimation of multidimensional views of data warehouse is a fundamental step for data cube design, warehouse storage planning, and query optimization.
对数据仓库多维视图进行容量估计是数据立方体设计、数据仓库存储规划和实施查询优化的基础。
Although the closed cube is a high-efficiency and important technology for data cube compression, there is no research on its parallel algorithm at present.
封闭立方体是一种非常有效而重要的数据立方体压缩技术，目前还缺乏对其并行算法的研究。
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；
This thesis presents a data generalization algorithm based on data cube. The algorithm can clean the data for data mining and im-prove efficiency of data mining.
该文提出了一种基于数据立方体的数据泛化算法用于数据预处理，能够为数据挖掘提供良好的数据环境，提高数据挖掘的有效性。
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.
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 process of data aggregating is the process of data cube materializing, so the maintenance of aggregated data can be transformed into the maintenance of data cube.
数据立方体的形成过程就是数据聚集的过程，对聚集数据的维护可以转化为对数据立方体的维护。
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