Multidimensional data model is the key technology of OLAP.
多维数据模型是联机分析处理技术的核心。
This paper defines an extended multidimensional data model.
提出了扩展多维数据模型。
According to the structural characteristic of multidimensional data model and OLAP's needs, a various granularity storage method has been presented to store historical data.
文中根据多维数据模型的结构特点以及OLAP需求提出了一种变粒度存储策略。
In order to support the applications of OLAP and data mining simultaneously, we gave the orientation that the multidimensional data model become the common data basic of OLAP and data mining,.
为了实现联机分析处理和数据挖掘的紧密结合，我们提出在多维数据模式的基础上同时支持OLAP应用和数据挖掘应用的思路。
In the end, research results of the dissertation are summarized, after that, some orientations of multidimensional data model and multidimensional computation for further research are also provided.
论文最后对全文进行了总结，并对多维数据模型及多维计算的研究方向进行了展望。
If the objective is to perform multidimensional data analysis, a dimensional data model would be the only choice here.
如果其目标是执行多维数据分析，那么维度数据模型就是这里的惟一选择。
For the business analysis system, the company needs to create multidimensional models based on the normalized data model.
对于业务分析系统，该公司需要基于规范化的数据模型创建多维模型。
Discovering multidimensional information based on a normalized data model.
基于规范化数据模型发现多维信息。
There are several factors that adversely affect performance, such as improper design of the data model (for example, creating a need for multidimensional arrays in your data model).
有几个因素会对性能产生负面影响，如数据模型的设计不正确（例如，在您的数据模型中产生对多维数组的需求）。
As a component of InfoSphere Warehouse, Cubing Services provide all the tools you need to design, deploy, and administer a multidimensional model of your relational data.
作为InfoSphereWarehouse的一个组件，CubingServices提供设计、部署和管理关系数据的多维度模型所需的所有工具。
According to the requirement of user's multidimensional data analysis, we design an private object-oriented multidimensional metadata model.
根据用户的多维数据分析需求，设计出面向对象的私有多维元模型。
Because this Mining Model is based on multidimensional data, you can create a new dimension from it.
因为此挖掘模型是基于多维数据的，所以您可以用此模型创建一个新的维度。
The Customer relationship management system based on data mining technique consists of data storage and colligation, multidimensional date analysis, data mining, model storage, method storage.
基于数据挖掘技术的客户关系管理系统由数据存储和综合、多维数据分析、数据挖掘、模型库、方法库组成。
In order to solve these problems, the dissertation studies data model of data warehouse and multidimensional computation in detail.
为了解决上述问题，本论文深入地研究了数据仓库的数据模型以及多维计算。
Data that resides in a cube. A Mining Model created from multidimensional data can be used to create a dimension and a Virtual cube.
驻留在多维数据集中的数据。用多维数据创建的挖掘模型可用于创建维度和虚拟多维数据集。
Global principal components analysis (GPCA) model is presented to the multidimensional dynamic data system and the time sequence solid data table is three-dimensionally synthesized and simplified.
对多维动态数据系统给出了全局主成分分析(GPCA)模型，并对时序立体数据表进行立体式的综合与简化。
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。
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。
Constructing data model is one of important steps for building data Warehouse. Multidimensional model is a conceptual model widely used in data Warehouse designing.
建立数据模型是构造数据仓库的重要步骤之一，多维数据模型是数据仓库设计中广泛采用的概念模型。
This paper discusses how to using model forecasting technology based on multidimensional data structure appropriately.
本文讨论了在多维数据结构的基础上，如何使用合适的模型预测技术的问题。
In this paper we discuss an academic base for building multidimensional analytical model of data warehouse and present a designing method of multidimensional analytical model of mine data warehouse.
介绍了建立数据仓库多维分析模型的理论基础，提出了矿山企业数据仓库多维分析模型的设计方法。
A Mining Model created from multidimensional data can be used to create a dimension and a Virtual Cube.
用多维数据创建的挖掘模型可用于创建维度和虚拟多维数据集。
One of our goals while designing the PivotTable component was to make the user interface and programming model consistent between tabular and multidimensional data sources.
我们设计透视表组件的目的之一，就是使表列数据源和多维数据源的用户界面和编程模型保持一致。
The part of data mining importantly studies the basic course of data mining, classification technology and late-model multidimensional data mining technology;
数据挖掘部分重点研究了数据挖掘的基本过程、分类技术和新型的多维数据挖掘技术；
The part of data mining importantly studies the basic course of data mining, classification technology and late-model multidimensional data mining technology;
数据挖掘部分重点研究了数据挖掘的基本过程、分类技术和新型的多维数据挖掘技术；
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