一种基于Web服务的分布式数据挖掘体系结构

分布式数据挖掘是数据挖掘领域的一个新兴研究课题,而其主要问题是知识共享和软组件重用.结合Web服务技术的跨平台、统一数据表示格式以及可实现软组件重用和数据重用等优点,文中提出了一种基于Web服务的分布式数据挖掘体系,可实现分布式异构环境下的大容量数据的数据挖掘,旨在对异构数据库的数据挖掘进行一些有意义的探讨.     

.NET平台下电子商务数据挖掘服务设计与实现

根据电子商务环境中分布式和异构性数据挖掘服务需求,设计了基于移动Agent和Web Service的五层分布式数据挖掘服务框架,实现数据挖掘服务与电子商务系统的松散耦合。从整体框架给出数据挖掘服务质量评价体系,包括支撑性服务质量评价和算法服务质量评价。具体分析了.NET平台下移动Agent的迁移和WCF技术创建数据挖掘服务过程组件的实现。     

基于Web服务技术实现异构环境下的互操作

分布式技术重点解决异构环境下的互操作性，传统的分布式异构环境操作不但烦琐，而且通用性很差。提出了基于Web服务技术进行异构环境下的互操作观点，介绍了Web服务的体系结构和相关主要技术，分析了当前流行的分布式组件技术及其互操作的缺陷后，给出了应用Web服务技术实现互操作的模型，并结合一个基于J2EE实例系统介绍了具体实现细节。Web服务将会成为下一代网络计算和企业应用的必然趋势。     

基于异构Web服务的虚拟实验平台

目前大部分虚拟实验平台缺乏对异构组件的支持。为此,提出一种基于异构Web服务的虚拟实验平台,该平台采用Web服务技术,以其语言独立性、平台无关性等特点,将异构组件进行Web服务化封装,解决异构组件的通信问题,提高开发效率,实现组件重用。介绍系统平台的架构模型和系统实现的关键技术,讨论对异构组件进行服务化封装及实验流程执行引擎的设计与实现过程,并以该平台为基础,通过Web服务封装将C++和Matlab开发的图像处理组件集成到系统中,构建数字图像处理虚拟实验室。     

结合Web服务技术探究数据挖掘体系的构建思路

随着信息技术的不断发展,传统的主要面向集中的数据源的数据挖掘系统已经不能满足时代发展的需要,web分布式数据挖掘技术应运而生,作为新兴的数据挖掘技术,建立了分布式计算平台,解决了传统数据挖掘技术中常见的操作困难、效率低、传输过程中安全性不能得到保证等问题。在web服务的基础上建立的分布式数据挖掘体系,可以挖掘出分布式异构环境下的大量数据,并且对数据隐私保护起着重要的作用。这样不仅使分布式数据挖掘系统处理大规模分布式异构数据得到用户的满足,也大大提高了系统的安全性、可扩展性、可交互性等。本文内容主要涉及到web服务、web挖掘技术的概念、于Web服务的分布式数据挖掘体系结构。     

分布式环境下一种基于服务的系统设计方法

基于SOA的软件开发是分布式异构软件系统构造方法和环境的新发展阶段。文章介绍了一种基于SOA的CBSD方法,提出一种基于业务的服务模型,阐述了服务构件的管理和信息交互方式。将业务提炼成服务构件和服务边界组件,用业务服务包重用的思想提出了一种基于服务边界组件的服务组装方法,解决了异构分布式环境下业务重组和软件重用的问题,用实例说明了该方法并进行了分析。     

分布式分析服务及其在设备故障诊断中的应用

提出了一个基于Web Service和XML技术的分布式分析服务系统,并将其应用到企业的设备故障诊断决策中。解决了传统分析服务重组性、重用性差,不支持异构的、分布的数据和系统等问题。     

基于J2EE的数据挖掘算法组件库设计

为了方便和加快数据挖掘算法的开发、交流和工程化、商业化，介绍了如何应用面向组件的软件设计思想，通过对算法结构的组件化分析，把数据挖掘算法分解为即相互联系、叉相对独立的算法组件模块，并应用J2EE成熟的组件和分布式处理技术Enterprise JavaBean，构建一个可重用的并具有良好可扩展性的数据挖掘算法组件库。组件库的设计可以提高数据挖掘算法的重用率，加快数据仓库项目的开发速度，并且降低软件开发的成本。     

一种基于COM+的分布式数据访问组件

根据数据访问的特点，利用ADO设计了一种基于COM 的数据访问组件，提供了数据操作接口，从而简化了数据访问，并同时能够将各数据库进行分布式处理、平衡数据流量、集成异构数据。组件在三层体系构架中处于中间层，隔离了用户界面层和数据库系统，逻辑清晰，可重用性强。     

异构组件互操作研究

目前工业级的分布式组件模型有:DCOM,CORBA以及EJB.不同组件模型间缺乏统一的互操作标准,形成了企业内部应用集成和跨企业应用互操作的瓶颈,总结了目前实现异构组件互操作的主要方法并分析了其不足.Web Services的出现为实现异构组件互操作提供了新的契机,提出了基于Web Services的异构组件互操作方案,并以CORBA为例,说明了分布式组件与Web Services的集成.     

Distributed data mining patterns and services: an architecture and experiments

Distributed data mining implements techniques for analyzing data on distributed computing systems by exploiting data distribution and parallel algorithms. The grid is a computing infrastructure for implementing distributed high‐performance applications and solving complex problems, offering effective support to the implementation and use of data mining and knowledge discovery systems. The Web Services Resource Framework has become the standard for the implementation of grid services and applications, and it can be exploited for developing high‐level services for distributed data mining applications. This paper describes how distributed data mining patterns, such as collective learning, ensemble learning, and meta‐learning models, can be implemented as Web Services Resource Framework mining services by exploiting the grid infrastructure. The goal of this work was to design a distributed architectural model that can be exploited for different distributed mining patterns deployed as grid services for the analysis of dispersed data sources. In order to validate such an approach, we presented also the implementation of two clustering algorithms on the developed architecture. In particular, the distributed k‐means and distributed expectation maximization were exploited as pilot examples to show the suitability of the implemented service‐oriented framework. An extensive evaluation of its performance was provided. Copyright © 2011 John Wiley & Sons, Ltd.     

The Weka4WS framework for distributed data mining in service‐oriented Grids

The service‐oriented architecture paradigm can be exploited for the implementation of data and knowledge‐based applications in distributed environments. The Web services resource framework (WSRF) has recently emerged as the standard for the implementation of Grid services and applications. WSRF can be exploited for developing high‐level services for distributed data mining applications. This paper describes Weka4WS, a framework that extends the widely used open source Weka toolkit to support distributed data mining on WSRF‐enabled Grids. Weka4WS adopts the WSRF technology for running remote data mining algorithms and managing distributed computations. The Weka4WS user interface supports the execution of both local and remote data mining tasks. On every computing node, a WSRF‐compliant Web service is used to expose all the data mining algorithms provided by the Weka library. The paper describes the design and implementation of Weka4WS using the WSRF libraries and services provided by Globus Toolkit 4. A performance analysis of Weka4WS for executing distributed data mining tasks in different network scenarios is presented. Copyright © 2008 John Wiley & Sons, Ltd.     

Service-Oriented Distributed Data Mining

Data mining research currently faces two great challenges: how to embrace data mining services with just-in-time and autonomous properties and how to mine distributed and privacy-protected data. To address these problems, the authors adopt the Business Process Execution Language for Web Services in a service oriented distributed data mining (DDM) platform to choreograph DDM component services and fulfill global data mining requirements. They also use the learning-from-abstraction methodology to achieve privacy-preserving DDM. Finally,they illustrate how localized autonomy on privacy-policy enforcement plusa bidding process can help the service-oriented system self-organize.     

Understanding Web services

Web services are a new breed of Web applications. These independent application components are published on to the Web in such a way that other Web applications can find and use them. They take the Web to its next stage of evolution, in which software components can discover other software components and conduct business transactions. Examples of Web services include a credit card service that processes credit card transactions for a given account number, a market data service that provides stock market data associated with a specified stock symbol, and an airline service that provides flight schedule, availability, and reservation functionalities. Major vendors like IBM, Microsoft, Hewlett-Packard, and Sun, among others, are investing heavily in Web services technology. Ranging from simple to complex, Web services bring the promise of flexible, open-standards-based, distributed computing to the Internet     

IrisNet: an architecture for a worldwide sensor Web

We discuss about the IrisNet (Internet-scale resource-intensive sensor network services) project at Intel Research, we design an architecture and build a system that enable easy deployment of such wide-area sensing services. We aim to provide the missing software components for realizing a worldwide sensor Web. Wide-area architectures for pervasive sensing enable a new generation of powerful distributed sensing services. A worldwide sensor Web, in which users can query, as a single unit, vast quantities of data from thousands or even millions of widely distributed, heterogeneous sensors. Internet-connected PCs that source sensor feeds and cooperate to answer users' queries will form the global sensor Web's backbone. Developers of wide-area sensing services (service authors) deploy the services on this distributed infrastructure.     

基于移动Agent的GIS动态服务技术

利用Web服务和Agent特性来解决空间信息GIS领域的动态服务问题.提出了基于Web和Agent的GIS动态服务模型,阐述了动态服务原理和相关的实现技术,特别是利用创建、迁移GISAgent和构件下载策略,以及GISAgent和Web服务的交互操作和集成技术,实现了用功能构件的移动代替海量空间数据的移动,提高了分布式网络的计算速度,改进了空间数据处理的瓶颈问题.     

Evolving a “Wise” integration system for e-commerce transactions

The Web facilitates a global marketplace that provides an economic platform for application developers, merchants and customers to exchange goods and services from a wide range of domains. As a result, large volume of data now resides on the Web. Access to data distributed over the Web is becoming increasingly difficult because of information overload. A system that transcends the amalgamation of data, and provides easy access to data distributed over the Web is necessary. We present WISE, a correctness preserving approach to integration of Web data sources. We describe components of WISE, including a flexible semistructured data model, a common-term vocabulary, and an efficient integration algorithm that automates the integration process. We formally specify these components and show that the global integrated schema is correct, complete, minimal, and understandable.     

一种分布式Web日志挖掘系统的设计与实现

Web挖掘已从早期的理论研究逐步转向实用阶段，可以利用对Web日志的挖掘发现用户的浏览模式、分析站点的使用情况。有效的Web日志挖掘工具是Web日志挖掘应用成功的关键。针对Web日志挖掘工具存在的问题与不足，提出了一种新颖的基于移动Agent技术的分布式Web日志挖掘系统（MAWLMS）的体系结构。重点阐述了移动代理层的设计，详细说明了移动Agent挖掘算法的设计与实现，包括最大向前路径识别算法和频繁遍历路径发现算法。最后对MAWLMS系统进行了验证，实验结果显示基于MAWLMS系统的分布式Web日志挖掘是可行且有效的。     

基于Agent的分布式数据挖掘系统

本文致力于研究如何将多代理技术应用于分布式数据挖掘中的课题,通过分析典型分布式结构的Web服务器日志,设计Web日志挖掘系统的体系结构、各个代理的具体研究设计工作等。本文将多代理技术与Web日志挖掘技术结合起来,一方面可以更清晰的进行数据挖掘系统的设计,另一方面可以充分利用多代理技术来提高数据挖掘的效率,从而对基于Agent的分布式数据挖掘系统的理论意义和应用层面价值进行探索。     

基于ASP.NET的Web服务性能优化

Web服务作为一种分布式计算技术模式,在电子商务、电子政务中发挥着重要的作用,其性能也日益引起大家的关注。针对Web服务的额外性能开销问题,从客户端应用程序和Web服务出发,通过优化服务调度策略、减少网络传输数据量和使用数据高速缓存三种策略提高Web服务性能。