Data cubes can be built in other ways.
数据立方体还可以用其他的方法构建。
The Data Explorer View is also used when adding report parameters and data cubes.
在增加报表参数和数据体时我们也会使用数据浏览视图。
What makes data cubes so valuable is that we can index the cube on one or more of its dimensions.
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。
It is a dense-based method and the low-dense data cubes are searched by clonal selection algorithm.
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。
This kind of model can apply to OLAP, and can improve the speed of OLAP queries in data cubes effectively.
这种模型可以应用到OLAP中，并能够有效提高OLAP查询多维数据的速度。
In practice, therefore, we often construct data cubes with many dimensions, but we tend to look at just three at a time.
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。
Efficiently realizing the data cubes computing is a powerful tool to increase the query efficiency of the data warehouse.
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。
Data cubes are multidimensional extensions of 2-d tables, just as in geometry a cube is a three-dimensional extension of a square.
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。
The dimension, the measure, the fact table, the muhidimension data and the data cubes of the system are also discussed in the article.
然后给出了库存数据仓库中相关的维表、度量、事实表以及整个数据立方的设计。
For example, predesigned dashboards and data cubes help staff track surgical outcomes and analyze benchmarks for practice management and clinical performance.
例如，预先设计的指示板和多维数据集帮助医疗人员跟踪手术结果和分析实践管理和临床绩效的基准。
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。
Thus data cubes can be extremely helpful in establishing trends and analyzing performance. In contrast, tables are best suited to reporting standardized operational scenarios.
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。
At present, the keystone in education domain is how to develop the data warehouse of teaching quality monitor system(TQMS), and mine some data cubes about teaching subject for rules.
本文以华侨大学为例，主要研究构建教学质量监控系统数据仓库所涉及的联机分析与数据挖掘技术。
A typical data integration architecture involves data from various source systems being extracted, transformed, and loaded (ETL) into data warehouses, marts, and cubes.
典型的数据集成架构把来自各个源系统的数据提取、转换和装载(etl)到数据仓库、数据集市和多维数据集中。
Only through this type of data source, cubes can be used.
只有通过这种类型的数据源，才能使用多维数据集。
When there are sales performance data being tracked in different cubes, a virtual cube provides a way to have the overall view by aggregating these separate cubes.
如果需要在不同的数据集中跟踪销售绩效数据，那么通过使用虚拟数据集合并各个数据集可以获得总体视图。
Data about what source systems were used to populate the data warehouse, marts, and cubes.
用来填充数据仓库、数据集市和多维数据集的源系统的相关信息。
TM1 itself will then build cubes in memory from the data that is held in the data Mart (see diagram on the right).
然后，TM 1自身将从DataMart(见上图右侧)中存储的数据在内存中构建多维数据集。
Marching cubes (MC) algorithm is a classical algorithm to extract iso-surface from regular volume data. At last, a medical images reconstruction system based on MC algorithm is developed.
移动立方体(MC)算法是基于规则体数据抽取等值面的经典算法，本文最后设计开发了一个基于MC算法的医学图像三维重建系统。
But you can conceptualize a four-dimensional data structure by thinking of the fourth dimension as multiple three-dimensional cubes.
但是您可以采用将一个四维数据结构看作是多个三维立方体的方式来理解四维数据结构。
Multi-feature cubes(MF-cubes) compute complex data mining queries involving multiple dependent aggregates at multiple granularities.
多特征方用于计算复杂数据挖掘查询，具有多粒度上多个依赖聚集计算的典型特点。
Multi-feature cubes(MF-cubes) compute complex data mining queries involving multiple dependent aggregates at multiple granularities.
多特征方用于计算复杂数据挖掘查询，具有多粒度上多个依赖聚集计算的典型特点。
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