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。
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。
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。
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。
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。
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。
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。
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。
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。
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。
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。
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。
This system can provide the queries of multi-dimensional data cube and also obtain the mining analysis though the mining algorithm for supporting decision-making.
系统可提供进销存多维数据立方体的查询，还可以通过挖掘算法进行挖掘分析以辅助决策。
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。
Utilization of conventional seismic data cube, coherence cube, fracture cube, the technique of amplitude property predicts the distribution of high quality reservoir.
综合利用常规地震数据体、相干体、裂缝体、振幅属性技术预测优质储层分布。
Constrained cube gradient mining is an important mining task and its goal is to extract the pairs of gradient-probe cell satisfy the gradient constraint from a data cube.
约束立方梯度挖掘是一项重要的挖掘任务，其主要目的是从数据立方中挖掘出满足梯度约束的梯度-探测元组对。
DEFINITION: A data cube is a type of multidimensional matrix that lets users explore and analyze a collection of data from many different perspectives, usually considering …
定义：数据立方体是一类多维矩阵，让用户从多个角度探索和分析数据集，通常是一次同时考虑三个因素（维度）…
This paper introduces partition method in data cube with different confidence, expatiates on multidimensional association rule data mining algorithm based on data cube partition.
介绍了在数据立方体上对于不同可信度的数据进行分块的方法，阐述了基于数据立方体分块的多维关联规则挖掘的算法。
The linchpin of multi-dimension analysis is to build multi-dimension data cube, essential of which is a multi-dimension array, and is the combination express of dimension and variable.
多维数据分析的关键是生成多维数据立方体，立方体实质上就是一个多维数组，是维和变量的组合表示。
Typically, online aggregation algorithms on multi-dimensional data need additional auxiliary data for estimation, which make the performance of the storage and maintenance of the data cube worse.
以往在数据立方体上实现的联机聚集往往需要附加空间来存储联机聚集估算所需要的信息，极大地影响了数据立方体的存储和维护性能。
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。
On this basis, the academia has put forward the idea of OLAM which had combined the advantages of the above two and offered the method of the interactive multidimensional data mining on the data cube.
在此基础上，学术界新提出了OLAM，它结合了二者的优势，提供在数据立方体上进行交互式多维数据挖掘的方法。
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。
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。
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。
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