基于库存控制的数据仓库与决策支持

通过对传统决策支持系统已经不能满足当今企业对决策支持的需要的分析,提出了采用数据仓库（DW）,联机分析处理（OLAP）及数据挖掘技术为企业提供决策必要性,设计了一种利用数据仓库（DW）技术和联机分析处理（OLAP）、数据挖掘（DM）等数据仓库工具提供库存控制决策支持的方案,并着重阐述了联机分析处理（OLAP）与数据挖掘是如何为企业库存控制提供决策支持的。     

数据仓库新解

数据仓库(DW)未来会怎么发展? 有人说是Active DW,也有人说是Realtime DW,还有人说是Dynanic DW, General-purpose DW等。从其历史发展过程来看,以下五个趋势是比较明显的。第一,从战略决策到战术决策的发展,对DW的实时性和可获得性提出了更高的要求。这就需要有更好的变化数据捕捉机制,可以快速方便地将数据抽取至数据仓库中。第二,需求更加多样化。它要求系统从架构和应用层次上去适应不同的需求,并且不同应用之间的     

基于DW的流通企业采购决策数据体系

采购决策是流通企业最重要的经营决策，它关系到流通企业的生存发展。文中在论述了采购决策一般过程有所需数据特点的基础上，提出了基于DW的流通企业采购决策支持的体系结构，并着重对决策数据体系给予了详尽分析，提出具体设计方法。     

浅谈数据仓库技术

本文叙述了数据仓库（DW：Data Warehouse)的定义、基本特性与结构,以及数据仓库在决策系统（DSS:Decision Support System)中的应用,并展望了该技术的发展前景。     

数据仓库在生产连续型企业库存控制中的应用

本文通过对生产连续型企业库存控制的特点分析，指出了仅仅利用传统的基于事务处理的管理信息系统和决策支持系统(DSS)解决该类库存控制决策分析问题的局限性，提出了采用数据仓库技术的必要性，并分析了传统决策支持系统的不足之处以及数据仓库(DW)技术对DSS的支持，设计了一种利用DW技术和数据挖掘(DM)、联机分析处理(OLAP)等
数据仓库工具提供库存控制决策支持的方案。     

SQLServer2005的数据挖掘技术在示范性院校管理决策支持系统中的应用

分析了高校MIS的特点及DM与OLAP的作用与功能。提出了高校建立基于数据挖掘技术的管理决策支持系统的途径与方法。即在建立DW基础上进行DM,并与OLAP相结合以提高系统辅助决策能力。     

基于KW、MW及DW的决策支持系统架构研究

SDSS是较为先进的决策支持系统，但由于其模型库中的模型固定不变，因此在实际应用中具有一定的局限性。提出了集成数据仓库DW、知识仓库KW和模型仓库MW作为底部架构的体系结构，能有效解决这一问题。该架构简化了模型获得和编码任务，增强了决策的有效性和智能性，提高了决策效率。     

集成在ERP采购管理中的数据挖掘技术

设计实现一个可扩展的、开放的集成在ERP采购管理系统中的数据挖掘系统.采用DM与DB/DW紧耦合方式构建,使用基于约束的交互式数据挖掘技术,将计算模型和工具分离,通过人机界面和知识推理接受用户决策需求并输出决策结果.     

光雕利刃BenQ DW1655

去年年底,我们曾经测试过BenQ的DW1650。DW1650的最大特色是加入了BenQ最新开发的技术—萨利刀(SolidBurn)刻录技术。该技术在廉价盘片的兼容性方面起到了十分重要的作用,通过自我学习式的刻录策略调整,有效地提高了刻录的成功率以及刻录质量。现在的DW1655则是DW1650的升级产品,相对于DW1650而言,DW1655增加了对光雕盘片的支持。其他的规格方面,DW1655与DW1650并无太大差异。这款DW1655支持16x刻录DVD-R/ R,8x刻录DVD-RW/ RW,8x刻录DVD RDL,4x刻录DVD-RDL。每一款支持光雕的刻录机都会给我们带来难以想象的乐趣。显然,…     

Dreamweaver插件秀（下）

DW的插件管理器 目前DW的最新版本是4．0版,它继承了前三个版本的插件类型。不过,考虑到日益壮大的DW用户群,同时照顾一些刚入门的初学者,为简化插件的安装,进一步提高Dw的易用性,Macromedia公司又自行开发了DW插件管理器。     

基于ODS的数据仓库模型研究

ODS的主要作用是提供实时访问和战术分析支持。通过比较各种基于ODS的数据仓库体系结构模型，提出具有通用性、可扩展性、灵活性和高性能的动态数据仓库体系结构模型，它能满足银行客户关系管理应用中业务和数据管理的要求。     

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.     

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.     

Incremental Design of a Data Warehouse

A data warehouse (DW) can be seen as a set of materialized views defined over remote base relations. When a query is posed, it is evaluated locally, using the materialized views, without accessing the original information sources. The DWs are dynamic entities that evolve continuously over time. As time passes, new queries need to be answered by them. Some of these queries can be answered using exclusively the materialized views. In general though new views need to be added to the DW.In this paper we investigate the problem of incrementally designing a DW when new queries need to be answered and possibly extra space is allocated for view materialization. Based on an AND/OR dag representation of multiple queries, we model the problem as a state space search problem. We design incremental algorithms for selecting a set of new views to additionally materialize in the DW that: (a) fits in the extra space, (b) allows a complete rewriting of the new queries over the materialized views, and (c) minimizes the combined new query evaluation and new view maintenance cost. Finally, we discuss methods for pruning the search space so that efficiency is improved.     

The automatic creation of OLAP cube using an MDA approach

The Model‐Driven Architecture (MDA) is an approach that aligns modeling and automation for software development. By applying such an approach to data warehouse (DW) projects, we can minimize a great deal of time and cost. Furthermore, most of OnLine Analytical Processing (OLAP) platforms seem to be like black boxes that provide wizards only to business intelligence developers to create and manipulate OLAP objects without allowing their sustainability and migration from a platform to another. That is why many works in the literature have proposed using the MDA approach in DW projects. However, most of them have mainly focused on the generation of the DW relational model from the conceptual one, and they overlooked the OLAP model and the cube implementation. To deal with this problem, we propose in this paper an MDA solution to automate the process of getting OLAP cube and its implementation through a set of metamodels and automatic transformations among them. In fact, the proposal generates the OLAP and DW relational models (PSMs) from the conceptual one, using also a PDM model that describes the target business intelligence platform. After that, the source code to create the cube is got from both PSM models. For this aim, we define a set of transformation rules implemented using the Atlas transformation language. Finally, a case study will be provided to validate our approach.     

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.     

基于数据仓库的校园管理与决策支持系统的设计

结合学校管理的一些特性 ,将数据仓库和数据挖掘的技术引入学校信息管理与决策支持系统中 ,分析了在校园事务管理内设计和实现数据仓库的具体问题 ,给出了一个系统实现的设计思路。     

Detecting redundant materialized views in data warehouse evolution

A data warehouse (DW) can be abstractly seen as a set of materialized views defined over a set of remote data sources. A DW is intended to satisfy a set of queries. The views materialized in a DW relate to each other in a complex manner, through common subexpressions, in order to guarantee high query performance and low view maintenance cost. DWs are time varying. As time passes new materialized views are added in order to satisfy new queries, or for performance reasons, while old queries are dropped. The evolution of a DW can result in a redundant set of materialized views. In this paper, we address the problem of detecting redundant materialized views in a given DW view selection, that is, materialized views that can be removed from DW without negatively affecting the query evaluation or the view maintenance process. Using an AND/OR dag representation for multiple queries and views, we first formalize the process of propagating source relation changes to the materialized views by exploiting common subexpressions between views and by using other materialized views that are not affected by these changes. Then, we provide an algorithm for detecting materialized views that are not needed in the process of propagating source relation changes to the DW. We also show how trivially redundant views can be identified in this process. Finally, we use these results to provide a procedure for detecting materialized views that are redundant in a DW. Our approach considers a broad class of views that includes grouping/aggregation views and is not dependent on a specific cost model.