基于多Agent的主动数据仓库的研究

传统的数据仓库只能分析历史数据且数据抽取周期过长,极大地降低了企业的应变能力。实时数据仓库的出现改变了这个局面。以此为基础的基于多Agent系统的四层数据仓库体系结构(MART-DW),一方面提高了数据仓库的主动性和实时性,另一方面也增强了系统的伸缩性,能够更加有效地管理分布式集成环境。     

ERP环境下的数据仓库系统研究

ERP环境下的数据仓库不仅要满足传统数据仓库对OLAP的支持,还要满足用户对常规事务的实时、主动决策。文章提出了一种ERP环境下的零等待数据仓库系统结构,利用多Agent系统的交互性和协作性等特点实现了数据仓库系统中连续数据集成和主动决策的功能。     

一种Agent协同规范及其应用研究

介绍了一种设计Agent间协同的方法学——AAF协同规范,以主动实时数据仓库系统为例,运用此规范对数据仓库系统中的Agent协同进行分析和描述,从而构建一个基于Agent的数据仓库系统。     

基于Agent的商务智能模型研究

针对传统的商务智能系统存在的被动性和集成困难的缺点,提出一个基于Agent的商务智能模型,可利用Agent建立和维护数据仓库,并进行数据挖掘.给出了其主动监控商务环境原理,以及对推理规则的定义,从中找到决策支持信息,最后结合了一个出版管理系统的应用进行了实践.     

基于Agent与数据挖掘的分布式入侵检测系统

针对目前计算机入侵检测系统中存在的不足．文中构建了一个基于Agent和数据挖掘技术的分布式入侵检测系统。这个系统引入移动Agent使入侵检测较好地适应了分布式的环境，采用数据挖掘技术使检测系统能够更加快速有效地发现入侵行为，明显地提高了检测系统的实时性。它还把误用检测和异常检测溶为一体，把基于主机和基于网络的入侵检测进行有机结合，具有良好的可扩展性、灵活性、鲁棒性、安全性、实时性、自适应性和检测的准确性。     

一种实时Agent体系结构

提出了一种应用于实时环境的Agent体系结构,集成了慎思式和反应式Agent的主要行为特征,支持多Agent之间的协同求解,并在内部机制上采用集中控制和规划缓冲等技术满足系统对实时性和稳定性的需要。最后给出了该体系结构在实时监控系统中的一个具体应用,论证了其设计的合理性。     

基于Agent与数据挖掘的分布式入侵检测系统

针对目前计算机入侵检测系统中存在的不足,文中构建了一个基于Agent和数据挖掘技术的分布式入侵检测系统.这个系统引入移动Agent使入侵检测较好地适应了分布式的环境,采用数据挖掘技术使检测系统能够更加快速有效地发现入侵行为,明显地提高了检测系统的实时性.它还把误用检测和异常检测溶为一体,把基于主机和基于网络的入侵检测进行有机结合,具有良好的可扩展性、灵活性、鲁棒性、安全性、实时性、自适应性和检测的准确性.     

Real-Time Workshop实时仿真研究与应用

MATLAB通过组件Simulink提供了一个动态系统建模、仿真和综合分析的集成环境,可以在控制领域中建模并运行仿真,却缺少实时仿真支持.MATLAB工具Real-Time Workshop可以从图形模型生成ANSI C源代码,下载此代码在实时操作系统中运行可以实现实时仿真,但是目前市场上的实时操作系统在开发工具、用户界面等方面存在诸多不足.RTX扩展Windows在提供实时性的同时又可以利用Windows丰富系统资源.利用Real-Time Workshop产生C代码,把要求实时性的代码分离出来放在RTX实时子环境下运行,可以实现实时仿真.实验验证了此方法有较好的实时性.     

基于Agent的数据仓库的研究

结合可移动的软件Agent技术,提出了基于Agent的数据仓库。它的提出使得用户可以更加有效地管理分布的、异构的集成环境。着重讨论了基于Agent的数据仓库的体系结构,并在此基础上讨论了一些实现中的问题。     

基于Agent的分布式系统数据集成方式的研究

本文讨论了分布式环境下的数据集成系统的特点和存在的问题。将Agent技术应用于邮件通讯的传输和管理中,提出了一种基于Agent的数据集成框架,对该集成框架的运行机制和具有的特点进行了研究。将该集成框架应用于实际项目,对其实现过程进行了阐述,并对应用的结果进行了说明。     

Web数据仓库的异步迭代查询处理方法

数据仓库信息量的飞速膨胀对数据仓库提出了巨大挑战.如何提高Web环境下数据仓库的查询效率成为数据仓库研究领域重要的研究问题.对Web数据仓库的体系结构和查询方法进行了研究和探讨.在分析几种Web数据仓库实现方法的基础上,提出了一种Web数据仓库的层次体系结构,并在此基础上提出了Web数据仓库的异步迭代查询方法.该方法充分利用了流水线并行技术,在Web数据仓库的查询处理过程中不同层次的结点以流水线方式运行,并行完成查询的处理,提高了查询效率.理论分析表明,该方法可以有效地提高Web数据仓库的查询效率.     

An empirical investigation of the effects of data warehousing on decision performance

Organizations implement data warehouses to overcome the limitations of DSS by adding this database component and thereby improve decision performance. However, no empirical evidence is available to show the effects of a data warehouse (DW) on decision quality and performance. To examine this, a laboratory experiment was conducted. The data warehouse variables considered were the time horizon of the data and its level of aggregation.It was found that using a full data warehouse resulted in significantly better performance and that using it resulted in better performance than using a partial data warehouse (long-time history with no aggregated data). However, using a partial data warehouse was not significantly better than not using a data warehouse at all.     

Data warehouse enhancement: A semantic cube model approach

Many data warehouse systems have been developed recently, yet data warehouse practice is not sufficiently sophisticated for practical usage. Most data warehouse systems have some limitations in terms of flexibility, efficiency, and scalability. In particular, the sizes of these data warehouses are forever growing and becoming overloaded with data, a scenario that leads to difficulties in data maintenance and data analysis. This research focuses on data-information integration between data cubes. This research might contribute to the resolution of two concerns: the problem of redundancy and the problem of data cubes’ independent information. This work presents a semantic cube model, which extends object-oriented technology to data warehouses and which enables users to design the generalization relationship between different cubes. In this regard, this work’s objectives are to improve the performance of query integrity and to reduce data duplication in data warehouse. To deal with the handling of increasing data volume in data warehouses, we discovered important inter-relationships that hold among data cubes, that facilitate information integration, and that prevent the loss of data semantics.     

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.     

On the evaluation of AGVS-based warehouse operation performance

An automated guided vehicle system (AGVS) is an advanced material handling system widely used in various automated systems, particularly in e-commerce warehouses. The warehouse operation performance, mainly focusing on transportation efficiency, is affected by many factors, such as traffic-control policies and warehouse layouts. Based on the digraph theory and real-time control, two effective traffic-control policies with polynomial-time complexity are proposed to avoid the collision and to solve deadlocks. To accommodate more complicated situations, different types of warehouse layouts are tested. Extensive simulations are carried out to study the effects of policies, warehouse layouts, task densities and timing of an AGV to apply for a resource on the evaluation of the AGVS-based warehouse operation performance, which provide guidelines for warehouse designers.     

A formal framework for query decomposition and knowledge integration in data warehouse federations

A federation of data warehouses is understood as a set of data warehouses, which can be processed as a whole in the logic level. Physically, the federation does not gather data into one place. This paper presents a formal framework for data and knowledge processing in data warehouse federations. The management system for a data warehouse federation consists of an user interface enabling presentation of user queries, a program for query decomposition and a program for integrating knowledge coming from different data warehouses as the answers to a user query. We propose a model for query decomposition process and knowledge integration. It contains also the algorithm for knowledge inconstancy processing. This kind of inconsistency often occurs since very often the knowledge extracted from different data warehouses refers to the same subject, but is not consistent.     

基于统一视图的数据仓库体系结构及其构建方法*

为了构建支持企业决策分析的数据仓库,分析了传统数据仓库模型的局限性,提出了一个基于统一视图模型的数据仓库体系结构。该体系结构是在传统数据仓库模型的数据源和数据仓库之间增加一个统一标准层,并利用统一视图—资源数据和数据仓库—统一视图的两级映射,保证了数据的透明访问和模型本身良好的可用性,进而支持灵活的多数据仓库的构建。基于该体系结构,给出了统一视图模型的建立和数据仓库三层之间两级映射的方法,提出了一种新的基于统一视图模型的数据映射—抽取—装载数据仓库ETL建模过程,并开发了相应的数据仓库构建系统。应用表明,     

Partitioning methods for multi-version XML data warehouses

Due to an explosive increase of XML documents, it is imperative to manage XML data in an XML data warehouse. XML warehousing imposes challenges, which are not found in the relational data warehouses. In this paper, we firstly present a framework to build an XML data warehouse schema. For the purpose of scalability due to the increase of data volume, we propose a number of partitioning techniques for multi-version XML data warehouses, including document based partitioning, schema based partitioning, and cascaded (mixed) partitioning model. Finally, we formulate cost models to evaluate various types of queries for an XML data warehouse.     

Capturing Delays and Valid Times in Data Warehouses—Towards Timely Consistent Analyses

Real-world changes are generally discovered delayed by computer systems. The typical update patterns for traditional data warehouses on an overnight or even weekly basis increase this propagation delay until the information is available to knowledge workers. Typically, traditional data warehouses focus on summarized data (at some level) rather than detailed data.For active data warehouse environments, detailed data about entities is required for checking the data conditions and triggering actions to automize routine decision tasks. Hence, keeping data current (by minimizing the latency from when data is captured until it is available to knowledge workers) and consistent in that context is a difficult task.In this paper we present an approach for modeling conceptual time consistency problems and introduce a data model that deals with timely delays. It supports knowledge workers in finding out, why (or why not) an active system responded to acertain state of the data. Therefore, the model enables analytical processing of detailed data (enhanced by valid time) based on a knowledge state at a specific time. All states that were not yet known by the system at that point in time are consistently ignored. This enables timely consistent analyses by considering that the validity of detailed data and aggregates can be restricted to time intervals only, due to frequent updates and late-arriving information.