数据仓库的体系结构与设计策略

文中介绍了数据仓库系统的体系结构,给出了数据仓库的实施步骤和设计中需要考虑的因素。     

数据仓库设计过程研究

从数据仓库与操作型数据库的区别出发，借鉴传统数据库设计的阶段划分思想给出了一种数据仓库设计方法，并明确了各步骤间的关系及其输入／输出和完成的工作。     

数据仓库质量及其应用

给出了数据仓库质量元模型的建模方法，提出了实现数据仓库应用质量、数据仓库数据质量的具体方法和数据防火墙的概念。     

数据仓库技术的研究

数据仓库（Data Warehouse）技术支持现代企业在决策中更迅速地、更准确地获得所需信息,是数据库技术的一个新的发展方向.本文阐述了数据仓库的基本概念、区别于数据库的几个特征,详细介绍了它的基本体系结构,最后对数据仓库技术的典型应用进行了简单介绍.     

智能数据仓库的设计方法

商业智能的深入实施和应用是中国银行业的必然选择,但是只有把技术的发展与业务需求真正的融合起来,商业智能的实际价值才能得到的体现。     

数据仓库设计的一种有效方法

通过数据仓库和数据库设计的比较,该文提出了一种基于数据库设计思想的数据仓库设计方法,给出了设计步骤,并介绍了源数据分析。同时,该方法在实际应用中取得了满意的效果。     

数据仓库技术及其实现

数据仓库是近年来信息领域发展迅速的一门学科.该文就数据仓库产生的背景,数据仓库的概念、基本特征体系结构进行了论述,并提出了实现数据仓库的基本步骤和应注意的问题.     

地理信息数据仓库技术的研究

在描述地理信息系统（GIS）功能特点和数据仓库技术的基础上，给出了GIS数据仓库的体系结构，并讨论了实现GIS数据仓库的关键技术。     

数据仓库技术的研究

数据仓库(DataWarehouse)技术支持现代企业在决策中更迅速地、更准确地获得所需信息,是数据库技术的一个新的发展方向。本文阐述了数据仓库的基本概念、区别于数据库的几个特征,详细介绍了它的基本体系结构,最后对数据仓库技术的典型应用进行了简单介绍。     

Modelling and querying geographical data warehouses

A number of proposals for integrating geographical (Geographical Information Systems—GIS) and multidimensional (data warehouse—DW and online analytical processing—OLAP) processing are found in the database literature. However, most of the current approaches do not take into account the use of a GDW (geographical data warehouse) metamodel or query language to make available the simultaneous specification of multidimensional and spatial operators. To address this, this paper discusses the UML class diagram of a GDW metamodel and proposes its formal specifications. We then present a formal metamodel for a geographical data cube and propose the Geographical Multidimensional Query Language (GeoMDQL) as well. GeoMDQL is based on well-known standards such as the MultiDimensional eXpressions (MDX) language and OGC simple features specification for SQL and has been specifically defined for spatial OLAP environments based on a GDW. We also present the GeoMDQL syntax and a discussion regarding the taxonomy of GeoMDQL query types. Additionally, aspects related to the GeoMDQL architecture implementation are described, along with a case study involving the Brazilian public healthcare system in order to illustrate the proposed query language.     

Efficient aggregation algorithms on very large compressed data warehouses

Multidimensional aggregation is a dominant operation on data warehouses for on-line analytical processing(OLAP).Many efficinet algorithms to compute multidimensional aggregation on relational database based data warehouses have been developed.However,to our knowledge,there is nothing to date in the literature about aggregation algorithms on multidimensional data warehouses that store datasets in mulitidimensional arrays rather than in tables.This paper presents a set of multidimensional aggregation algorithms on very large and compressed multidimensional data warehouses.These algorithms operate directly on compressed datasets in multidimensional data warehouses without the need to first decompress them.They are applicable to a variety of data compression methods.The algorithms have different performance behavior as a function of dataset parameters,sizes of out puts and ain memory availability.The algorithms are described and analyzed with respect to the I/O and CPU costs,A decision procedure to select the most efficient algorithm ,given an aggregation request,is also proposed.The analytical and experimental results show that the algorithms are more efficient than the traditional aggregation algorithms.     

大型数据仓库实现技术的研究

大型数据仓库是实现海量数据存储的有效途径,但在大型数据仓库的实现中存在很多问题。在分析问题的基础上,对大型数据仓库的实现问题提出了一定的解决策略,对其中的几个关键技术即数据立方体的有效计算、增量式更新维护、索引优化、故障恢复、模式设计和查询优化的代价模型及元数据的定义和管理等作了研究。     

数据仓库索引启发式查询优化方法

在大型数据仓库查询过程中,经常涉及多事实表的连接操作。传统的查询优化方法是在计算多关系连接时尽可能地减少中间关系的大小,并没有考虑到数据仓库中数据的海量,以读为主且事实表一般建有索引的特点,往往无法取得最优的效果。针对数据仓库查询的特点,提出了一种利用索引加快查询的启发式优化方法。理论分析与实验表明,该方法在查询处理代价和执行时间上都明显减少,方法具有有效性。     

数据立方体实体化的多目标遗传算法

多维数据实视图选择问题是一个NP完全问题。提出一种基于约束的多目标优化遗传算法,将查询代价和维护代价分开考虑,更有效地解决复杂的实视图选择问题。实验结果表明,该算法具有更好的性能,特别是在获得的Pareto前沿的分布性上。     

Two approaches to the integration of heterogeneous data warehouses

In this paper we address the problem of integrating independent and possibly heterogeneous data warehouses, a problem that has received little attention so far, but that arises very often in practice. We start by tackling the basic issue of matching heterogeneous dimensions and provide a number of general properties that a dimension matching should fulfill. We then propose two different approaches to the problem of integration that try to enforce matchings satisfying these properties. The first approach refers to a scenario of loosely coupled integration, in which we just need to identify the common information between data sources and perform join operations over the original sources. The goal of the second approach is the derivation of a materialized view built by merging the sources, and refers to a scenario of tightly coupled integration in which queries are performed against the view. We also illustrate architecture and functionality of a practical system that we have developed to demonstrate the effectiveness of our integration strategies. A preliminary version this paper appeared, under the title “Integrating Heterogeneous Multidimensional Databases” [9], in 17th Int. Conference on Scientific and Statistical Database Management, 2005.     

Web数据仓库的逻辑设计

对于Web数据仓库,以前考虑的仅仅是Weblog日志数据的处理问题。给出了一种把Web的3种数据源Weblog日志数据、Marketing数据和Web元数据集成于数据仓库的逻辑设计模型。     

Specification-based data reduction in dimensional data warehouses

Many data warehouses contain massive amounts of data, accumulated over long periods of time. In some cases, it is necessary or desirable to either delete “old” data or to maintain the data at an aggregate level. This may be due to privacy concerns, in which case the data are aggregated to levels that ensure anonymity. Another reason is the desire to maintain a balance between the uses of data that change as the data age and the size of the data, thus avoiding overly large data warehouses. This paper presents effective techniques for data reduction that enable the gradual aggregation of detailed data as the data ages. With these techniques, data may be aggregated to higher levels as they age, enabling the maintenance of more compact, consolidated data and the compliance with privacy requirements. Special care is taken to avoid semantic problems in the aggregation process. The paper also describes the querying of the resulting data warehouses and an implementation strategy based on current database technology.     

Realizing active data warehouses with off‐the‐shelf database technology

Active data warehouses belong to a new category of decision support systems, which automate decision making for routine decision tasks and semi‐routine decision tasks. Just as active database systems extend conventional database systems with event–condition–action rules for integrity constraint enforcement or procedure execution, active data warehouses extend conventional data warehouses with analysis rules that mimic the work of an analyst during decision making. This paper demonstrates how analysis rules can be implemented on top of a passive relational data warehouse system by using commercially available database technology. Copyright © 2002 John Wiley & Sons, Ltd.