基于CWM的ODS元模型设计技术的研究与实践

在数据仓库系统的DB-ODS-DW三层体系结构中,ODS充当了承上启下的重要角色,但传统的ODS构建技术和元数据管理方法成本较高,而且在不同厂商的数据仓库和业务分析工具之间的互操作困难.以特种设备检验机构为背景,以特种设备事故分析需求为驱动,提出了一种基于公共仓库元模型的ODS元模型构建技术,进而使用JMI对元模型予以实现.该方法可提高模型的重用性,并对各工具间提供了公共的交换机制,降低了开发成本.     

基于广域网和异构环境的ODS数据更新策略

为满足信息处理的多层次需求，为企业日常决策分析者提供即时准确的全局数据视图，出现了DB-ODS-DW 3层体系结构，其中ODS承担了承上启下的重要作用。该文就ODS中的关键技术——数据更新策略进行了深入的研究，在对比分析了ODS传统数据更新策略的基础上，提出了一种基于网络和异构环境下的ODS数据更新策略，利用XML技术实现了异构数据库环境下变化数据的更新，以及变化数据文件的高效传输，并在实际应用中获得了理想的效果。     

开放数据共享(ODS)模型在电信NGOSS建设中的研究与实现

ODS是NGOSS提出的建立共享信息模型的通用框架，ODS分别从商业和系统两个视点描述了共享信息模型。ODS模型的建立使电信的OSS／BSS系统数据不再孤立，各个子系统能够实时的共享数据，而且保证了共享数据的一致性。本文对ODS棱心模型进行了分析和研究，提出了一种在NGOSS系统建设中简单的共享数据信息实现方法。     

ODS系统的设计与应用

ODS的出现弥补了DB-DW两层体系结构的不足,满足了企业信息处理多层次的要求.本文给出了ODS系统的设计,既实现了对操作型数据的存储,又满足了企业对分析型数据的需求.     

关于数据仓库一种新型体系结构的探讨

针对当前传统数据仓库DB-DW两层体系结构存在的不足,本文引入了ODS(Operational Data Store)方法,构建起DB-ODS-DW数据仓库三层体系结构;并在分析当前通用数据仓库逻辑建模方法不足的基础上,提出将第三范式(3NF)和星型模式两种建模方法相结合,设计和实现DB-ODS-DW体系结构.     

基于软件体系结构的用户界面设计方法

为实现软件体系结构指导下的软件工程设计，该文提出了一个在体系结构指导下、支持自动生成的用户界面工程开发方法。该方法把界面的体系结构作为工程模型的直接描述对象，在界面体系结构的引导和约束下，通过可视化用户干预建立界面的展示模型，在代码生成阶段采用了界面体系结构指导下的界面设计模式。研究表明，以体系结构为指导的软件模型化设计是可行的。     

基于Smart eVision的商业智能平台的研究

针对目前商业智能项目失败率高,本文提出基于虚拟仓库和ODS的商业智能体系结构,实施周期短,数据实时性高。在企业实施商业智能平台,可以及时帮助企业领导做出正确的决策。     

一种新型数据仓库体系的实现

针对当前流行数据仓库体系DB-DW存在的不足，引入ODS，构建起DB-ODS-DW数据仓库三层体系结构；并在分析当前通用数据仓库逻辑建模方法不足的基础上，提出将第三范式(Third Normal Form，3NF)和星型模式(Star-Schema)两种建模方法相结合来设计和实现DB-ODS-DW体系。     

基于应用区域边界体系结构的安全模型

基于信息系统的整体安全解决，国内外信息安全专家提出了信息安全保障、信息安全体系结构等概念，其中最著名的是美国国家安全局推出的《信息保障技术框架（IATF）》3．1版和国内专家提出的“三横三纵两个中心”信息安全体系结构。前不久，陈兴蜀在这两个信息安全体系结构思想指导下，提出了应用区域边界的安全体系结构。本文在此给出了该体系结构的安全模型。通过分析，我们认为：该模型的规则是合理的、安全的。通过构建信息安全体系结构模型，将推动信息安全的理论研究。     

大型复杂软件系统安全需求的体系结构模型

在开发基于Internet的大型复杂软件系统时，应该在体系结构层次上考虑业务需求和安全需求，而传统的体系结构没有专门针对安全需求的构件、连接件和体系结构风格的描述，因此在体系结构层次上描述安全需求还比较困难。本文首先论述了在体系结构层次上描述安全需求的必要性。然后，在传统体系结构单元——部件／连接件的基础上，引入了安全构件、半安全构件、安全连接件、半安全连接件等新的设计单元，并给出了这些设计单元形式化的语义和约束以及图形模型，解决了软件系统安全需求的构件表示方式。最后，用一个实例展示了软件系统安全需求的体系结构模型。     

基于ODS构建商业系统的即时OLAP应用

基于DB和DW的中间层ODS的数据存储技术,从应用的角度分析设计了一个商业系统的即时OLAP系统。通过使用ODSD克服了利用DW进行决策过于臃肿且不适合企业即时中层决策的问题。     

浅析通信行业中企业数据仓库的设计与实施

本文首先结合数据仓库的建设目标,提出了在系统规划时可以采用的若干总体性能指标.然后,依据业界两种主流数据仓库设计思想,对带有ODS的数据仓库逻辑架构作了描述;接着重点对各种主流数据仓库平台进行了对比;最后从技术规范的角度在数据仓库性能评价方面提供了若干参考建议.     

Data Warehousing Infusion and Organizational Effectiveness

Data warehousing (DW) has emerged as one of the most powerful technology innovations in recent years to support organization-wide decision making and has become a key component in the information technology (IT) infrastructure. Proponents of DW claim that its infusion can dramatically enhance the ability of businesses to improve the access, distribution, and sharing of information and provide managerial decision support for complex business questions. DW is also an enabling technology for data mining, customer-relationship management, and other business-intelligence applications. Although data warehouses have been around for quite some time, they have been plagued by high failure rates and limited spread or use. Drawing upon past research on the adoption and diffusion of innovations and on the implementation of information systems (IS), we examine the key organizational and innovation factors that influence the infusion (diffusion) of DW within organizations and also examine if more extensive infusion leads to improved organizational outcomes. In this paper, we conducted a field study, where two senior managers (one from IS and the other from a line function) from 117 companies participated, and developed a structural model to test the research hypotheses. The results indicate that four of the seven variables examined in this paper-organizational support, quality of the project management process, compatibility, and complexity-significantly influence the degree of infusion of DW and that the infusion, in turn, significantly influences organization-level benefits and stakeholder satisfaction. The findings of this paper have interesting implications for both research and practice in IT and DW infusion, as well as in the organization-level impact of the infusion of enterprise-wide infrastructural and decision support technologies such as DW.     

Multiversion join index for multiversion data warehouse

The data warehouse (DW) technology is developed in order to support the integration of external data sources (EDSs) for the purpose of advanced data analysis by On-Line Analytical Processing (OLAP) applications. Since contents and structures of integrated EDSs may evolve in time, the content and schema of a DW must evolve too in order to correctly reflect the evolution of EDSs. In order to manage a DW evolution, we developed the multiversion data warehouse (MVDW) approach. In this approach, different states of a DW are represented by the sequence of persistent DW versions that correspond either to the real world state or to a simulation scenario. Typically, OLAP applications execute star queries that join multiple fact and dimension tables. An important optimization technique for this kind of queries is based on join indexes. Since in the MVDW fact and dimension data are physically distributed among multiple DW versions, standard join indexes need extensions. In this paper we present the concept of a multiversion join index (MVJI) applicable to indexing dimension and fact tables in the MVDW. The MVJI has a two-level structure, where an upper level is used for indexing attributes and a lower level is used for indexing DW versions. The paper also presents the theoretical upper bound (pessimistic) analysis of the MVJI performance characteristic with respect to I/O operations. The analysis is followed by experimental evaluation. It shows that the MVJI increases a system performance for queries addressing multiple DW versions with exact match and range predicates.     

数据仓库技术在教育领域中的应用

数据仓库是数据挖掘过程中一个非常重要的环节,它涉及到数据的清理、整合等内容,而且,为交互式数据分析提供了OLAP工具。作为数据库技术最新发展的数据仓库已被广泛应用。探讨了什么是数据仓库及其性质、特点,并举出了在教育中的应用。     

ODS系统的应用与设计

业务数据商店(ODS)是基于数据仓库的决策支持技术的另一表现形式,它弥补了数据仓库对即时分析处理应用的不足。文章分析了数据库、ODS、数据仓库三者的技术特点,对面向应用的ODS分类结构以及在应用中所要注意的问题进行了阐述,并对业务数据商店系统设计的关键技术进行了研究。     

数据仓库实现过程及在线分析

数据仓库技术是近几年来出现并迅速发展的一种技术，它可以充分利用数据仓库中已存储的信息，帮助决策者进行决策。本文在介绍数据仓库的定义和数据仓库决策支持系统原理的基础上，详尽地讨论了数据仓库中的数据抽取与转换方法，并对数据仓库实现过程进行了实例探索和在线分析，最后根据数据挖掘方法进行了实际的应用。本文就是采用微软SQL Server 2000数据仓库技术设计了一商业用的数据仓库，使读者可清楚了解数据仓库的决策支持系统的实现过程。     

Integrating Data Warehouses with Web Data: A Survey

This paper surveys the most relevant research on combining Data Warehouse (DW) and Web data. It studies the XML technologies that are currently being used to integrate, store, query and retrieve web data, and their application to DWs. The paper reviews different DW distributed architectures and the use of XML languages as an integration tool in these systems. It also introduces the problem of dealing with semi-structured data in a DW. It studies Web data repositories, the design of multidimensional databases for XML data sources and the XML extensions of On-Line Analytical Processing techniques. The paper addresses the application of information retrieval technology in a DW to exploit text-rich documents collections. The authors hope that the paper will help to discover the main limitations and opportunities that offer the combination of the DW and the Web fields, as well as, to identify open research lines.     

Reconciling requirement-driven data warehouses with data sources via multidimensional normal forms

Successful data warehouse (DW) design needs to be based upon a requirement analysis phase in order to adequately represent the information needs of DW users. Moreover, since the DW integrates the information provided by data sources, it is also crucial to take these sources into account throughout the development process to obtain a consistent reconciliation of data sources and information needs. In this paper, we start by summarizing our approach to specify user requirements for data warehouses and to obtain a conceptual multidimensional model capturing these requirements. Then, we make use of the multidimensional normal forms to define a set of Query/View/Transformation (QVT) relations to assure that the conceptual multidimensional model obtained from user requirements agrees with the available data sources that will populate the DW. Thus, we propose a hybrid approach to develop DWs, i.e., we firstly obtain the conceptual multidimensional model of the DW from user requirements and then we verify and enforce its correctness against data sources by using a set of QVT relations based on multidimensional normal forms. Finally, we provide some snapshots of the CASE tool we have used to implement our QVT relations.     

Reconciling requirement-driven data warehouses with data sources via multidimensional normal forms

Successful data warehouse (DW) design needs to be based upon a requirement analysis phase in order to adequately represent the information needs of DW users. Moreover, since the DW integrates the information provided by data sources, it is also crucial to take these sources into account throughout the development process to obtain a consistent reconciliation of data sources and information needs. In this paper, we start by summarizing our approach to specify user requirements for data warehouses and to obtain a conceptual multidimensional model capturing these requirements. Then, we make use of the multidimensional normal forms to define a set of Query/View/Transformation (QVT) relations to assure that the conceptual multidimensional model obtained from user requirements agrees with the available data sources that will populate the DW. Thus, we propose a hybrid approach to develop DWs, i.e., we firstly obtain the conceptual multidimensional model of the DW from user requirements and then we verify and enforce its correctness against data sources by using a set of QVT relations based on multidimensional normal forms. Finally, we provide some snapshots of the CASE tool we have used to implement our QVT relations.