动态交叉表和数据立方体特性。
数据立方体还可以用其他的方法构建。
因此，通常不需要在数据立方体中进行计算。
Thus we normally don't perform calculations within a data cube.
动态交叉表及数据立方体(data cube)元素。
OLAP按照数据立方体模型组织多维数据，从而方便了查询。
OLAP organizes multidimensional data against data cube model, which is convenient to queries.
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。
Chapter 2 of data warehouse technology and the concept of a data cube is the theoretical foundation.
有效的实现数据立方体的计算是提高数据仓库查询效率的有力方法。
Efficiently realizing the data cubes computing is a powerful tool to increase the query efficiency of the data warehouse.
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。
This also means that we're not looking at real-time, dynamic data in a data cube.
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。
Data cube compressed storage is an important research content in large-scale database about data cube optimization calculation.
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。
It's important to keep in mind that the data in a data cube has already been processed and aggregated into cube form.
数据立方体之所以有价值，是因为我们能在一个或多个维度上给立方体做索引。
What makes data cubes so valuable is that we can index the cube on one or more of its dimensions.
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。
At last, based on a mathematical tool: algebraic system, this paper gives definition of spatial data cube.
数据立方体是二维表格的多维扩展，如同几何学中立方体是正方形的三维扩展一样。
Data cubes are multidimensional extensions of 2-d tables, just as in geometry a cube is a three-dimensional extension of a square.
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。
Methods: Analyze the characteristics of operational data, from the perspective of data model and cube data to organize and show the data.
在实际中，我们常常用很多个维度来构建数据立方体，但我们倾向于一次只看三个维度。
In practice, therefore, we often construct data cubes with many dimensions, but we tend to look at just three at a time.
这种方法使用了基于密度的孤立点挖掘的主要思想，用克隆选择算法进行数据立方体搜索。
It is a dense-based method and the low-dense data cubes are searched by clonal selection algorithm.
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。
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.
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。
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.
相对于其他类型的稀疏数据库，数据立方体往往会增加存储需求，有时会达到不能接受的程度。
As with other types of sparse databases, this tends to increase storage requirements, sometimes to unacceptable levels.
这样，在预测趋势和分析业绩时，数据立方体就非常有用，而表格最适合报告标准化的运作情况。
Thus data cubes can be extremely helpful in establishing trends and analyzing performance. In contrast, tables are best suited to reporting standardized operational scenarios.
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。
Size estimation of multidimensional views of data warehouse is a fundamental step for data cube design, warehouse storage planning, and query optimization.
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。
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.
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。
According to the practical context, this thesis raises a kind of hybrid data cube model and its query and incremental update algorithm.
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 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.
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。
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.
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.
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）…
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 …
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。
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。
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.
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