采用多种途径实现多维数据库的物理存储。
The paper realizes physical memory of multidimensional database by many ways.
多维数据库比关系型数据库产品价格更高。
Multidimensional products cost significantly more than standard relational products.
在模型设计完成后，在多维数据库中进行了应用。
After the model design, the multi-dimensional database has been applied.
多维数据库中的超立方体结构应用于对海量数据的多维分析中。
The cube in multidimensional database is used in the multidimensional analysis of the huge data.
通过对多维数据库的分析，提出多维技术可以应用到面向学科的知识仓库中。
Analyzing multidimensional database, the paper puts forward multidimensional technology which may be used in discipline-oriented knowledge warehouse.
与关系数据库相比，基于多维数组的多维数据库更适合表示和存储多维数据。
As compared to RDB (Relational DataBase), multidimensional data base (MDDB) which base on multidimensional arrays is more suitable to express and store multidimensional data.
加载数据至多维数据库的过程，需要消耗大量系统资源(内存、CPU等)。
Database loading consumes significant system resources and time, depending on data volume and the number of dimensions.
阐述了数据集市的基本概念，并结合实际例子探讨了一种用多维数据库实现数据集市的方法。
And describes a way to form a data market through multi-dimensional database combined with a practical case.
阐述了数据集市的基本概念，并结合实际例子探讨了一种用多维数据库实现数据集市的方法。
Explicates the basic theory and concept of data market. And describes a way to form a data market through multi-dimensional database combined with a practical case.
在严格数据仓库概念基础上，说明了数据仓库与多维数据库之间关系及数据仓库的特性和优势。
Based on the strict concept of data warehouse, the relations between data warehouse and multi dimensional database and the performances of data warehouse are explained.
需求管理系统以多维数据库做基础数据平台，其上要构建需求预测和战略用户协同等多个子系统。
Based on the data platform with multidimensional database, some applications such as demand forecasting and strategic synergy are needed.
文章首先对集装箱多式联运中所涉及的问题，影响的因素进行了分析，然后对多维数据库与相关技术进行了简单的介绍。
This paper analyzes the problems and factors which affect the container multimodal transportation and then produces the multi-dimensional database technology and related technology.
为了获得更好的性能和最小化空间消耗，我们建议使用数据库分区、范围分区、压缩和多维集群。
For best performance and minimal space consumption, we recommend using database partitioning, range partitioning, compression, and multi-dimensional clustering.
对诸多大公司而言，你不仅仅是顾客，而是用数据库中的多维信息描述的个体。
To many big companies, you aren't just a customer, but are described by multiple "dimensions" of information within a computer database.
这意味着每个已定义的多维数据集每分钟重新构建其元数据，从而引发处理开销和数据库操作。
This means that each defined cube rebuilds its metadata every minute and thus incurs processing overhead as well as database activity.
当您导入包含多维数据集模型的文件时，系统将提示您选择一个数据库。
You are prompted to select a database when you import the file containing your cube model.
AdvancedDBA考试涉及到联邦数据库、分区、具体化的查询表和多维集群表。
The Advanced DBA exam gets into federated databases, partitioning, materialized query tables, and multidimensional clustering tables.
零售企业可以使用多维模式来完成数据库部署，并使用Cubing或Cognos模型来完成业务智能部署。
The retail company can use the dimensional schema for database deployment and use the Cubing or Cognos model for business intelligence deployment.
由于DB2CubeViews能够捕捉 DB2数据库的多维结构和设计，所以元数据应该通过元数据桥导入 DB2Alphablox 中。
Since DB2 Cube Views can capture the multidimensional structure and design of DB2 database, the metadata should be imported into the DB2 Alphablox through the metadata bridge.
DB 2CubeViews用来在关系型DB 2数据库上创建多维数据集模型。
DB2 cube Views is used to create multidimensional cube model on top of relational DB2 database.
DB 2Cubeview元数据可以捕获DB 2数据库的多维结构和设计。
The DB2 Cube View metadata can capture the multidimensional structure and design of a DB2 database.
在导入包含安全模型的文件之前，使用AdministrationConsole将包含多维数据集模型的文件导入InfoSphere Warehouse控制数据库。
Before you import the file containing your security model, import the file containing your cube model into the InfoSphere Warehouse control database using the Administration Console.
您可以将更多特定于数据库的信息添加到多维物理数据模型，但是我们此处不做过多介绍。
You can add more database-specific information to the dimensional physical data model, but we will not introduce much here.
换句话说，TM 1多维数据集并不存储于SQL数据库中，而是从该数据库动态创建。
In other words, the TM1 cubes are not stored in the SQL database, but instead they are dynamically created from it.
数据库可以存储olap元数据，包括olap模型和多维数据集。
The database is enabled for storing OLAP metadata, including an OLAP models and cubes.
数据库可以包含物化查询表（MQT），即用于 DWEOLAP模型和多维数据集的预联结和预聚合的表。
The database may contain materialized query tables (MQTs), the pre-joined and pre-aggregated tables for DWE OLAP models and cubes.
为了根据不同的国家和语言构建单个多维数据集模型，需要将本地化数据存储在不同语言的数据库中，如图8所示。
To build the individual cube model according to the country and language, store localized data in database in different languages, as illustrated in Figure 8.
和上一版Cubing Services一样，这个数据库用于在用户查询多维数据集模型中的多维数据集时验证用户。
As in previous releases of Cubing Services, this database is used to authenticate users when they query the cubes in your cube model.
要让数据从Controller数据库传入TM 1多维数据集，需要一个中间数据库。
To allow data to pass from the Controller database into the TM1 cube an intermediate database is required.
多维索引技术是基于内容检索的图像数据库的关键技术。
Multidimensional indexing technology is the key technology of content-based retrieval in image database.
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