一种网格数据挖掘应用系统的设计

数据挖掘算法广泛地应用于数据分析。工业、科学和商业领域需要分析地理上分布的大量数据集，而网格能有效地提供高性能应用和分布式的基础设施。为了利用网格实现数据挖掘和知识表示，文中根据知识网格的概念，在Globus Toolkit的基础上．分析了知识网格的体系结构和它的主要组件，根据数据挖掘的过程设计了一种网格数据挖掘系统软件模型，并指出了该模型应提供的服务，这些服务会屏蔽所有关于网格底层的所有细节，使最终用户只关心知识发现的过程。     

基于知识网格服务的分布式数据挖掘研究

广域网中存在地理上分布的海量的各种数据,分析和处理这些数据需要利用高性能的分布式并行处理系统,网格能够满足这种要求.知识网格就是使用基本的网格服务(通信服务、信息服务、授权服务和资源管理服务)去建立特定的分布式并行知识发现工具和服务.结合知识网格特点,讨论了知识网格的体系结构和支持知识挖掘应用的服务集.运用分布式数据挖掘的元学习模型,给出了利用知识网格提供的知识挖掘服务实现分布式数据挖掘的过程.     

基于知识网格的数据挖掘

工业、科学、商务等领域的数据通常分布在不同的地方,需要在不同的地点对其进行分布式维护。只有使用计算功能超强的分布式、并行处理系统才能分析这些领域所产生的超大规模数据集。网格为分布式知识发现应用中的计算提供了有效支持。为了在网格上进行数据挖掘的开发,本文提供了一个称之为知识网格的系统,讨论如何应用知识网格设计实施数据挖掘应用,并说明如何搜索网格资源、编制软件和数据组件,以及数据挖掘应用在网格上的执行过程。     

基于知识网格的分布式数据挖掘

科学和工商业应用需要分析分布在各异构站点的海量数据。这就需要合适的分布式并行系统来存储和管理数据。网格为分布式数据挖掘和知识发现提供了有效的计算支持。文中在讨论知识网格体系结构的基础上，利用可视化网格应用环境VEGA实现了基于网格的分布式数据挖掘过程。     

基于知识网格的分布式数据挖掘

科学和工商业应用需要分析分布在各异构站点的海量数据。这就需要合适的分布式并行系统来存储和管理数据。网格为分布式数据挖掘和知识发现提供了有效的计算支持。文中在讨论知识网格体系结构的基础上,利用可视化网格应用环境VEGA实现了基于网格的分布式数据挖掘过程。     

通用知识网格下以用户为中心的数据挖掘本体研究

分布异构的海量数据挖掘是数据挖掘领域急待解决的课题，通用知识网格（UKB）架构模型用于在网格环境下创建大规模的分布式知识发现和知识集成系统。本体服务器是整个架构的核心模块，负责本体的管理和查询。数据挖掘本体服务是本体服务器提供的主要服务。本文主要介绍通用知识网格下以用户为中心的数据挖掘本体的设计和OWL实现。数据挖掘本体可满足各种不同领域、不同层次用户的知识发现服务，使系统具有开放性、可扩展性和高用户可用性。还介绍了一个反洗钱领域数据挖掘解决方案实例。     

基于知识网格的数据挖掘研究

目前,尽管基于网格计算、知识网格的数据挖掘技术都还不成熟,但随着研究的不断深入,数据挖掘的工具及其算法也必将在分布性、并行性、灵活性和有效性方面得到进一步发展和提高.伴随着网格和数据挖掘技术的不断提高,基于知识网格的数据挖掘技术将得到广泛的应用.文中在讨论知识网格体系结构的基础上实现了基于网格的分布式数据挖掘过程.     

科学数据挖掘网格服务框架*

在分析科学数据网格环境下数据挖掘之特点的基础上,提出了科学数据挖掘网格服务框架.科学数据挖掘网格服务以网格服务的形式提供了科学数据网格环境下的数据挖掘解决方案.与传统的数据挖掘系统相比,科学数据挖掘网格服务具有诸多优点,更适合科学数据网格和科学数据库环境.目前已经实际应用于几个数据库中,不仅具有简单的查询检索功能,而且可以进行数据统计分析及知识发现,进一步提高了科学数据网格服务的水平.     

基于知识网格的Apriori挖掘算法研究

在现有的网格和数据挖掘技术基础上,研究和分析了知识网格的整体框架,以及该框架下语义学习过程,基于知识网格平台下的语义学习思想,对经典的关联规则算法Apriori算法作了改进,使之适用于知识网格平台下的分布式关联规则挖掘,并对该算法进行了分析、测试和评估.     

基于知识网格的知识资源管理模式研究

知识网格是知识资源管理的前沿技术。为了更好地开发和推广这一技术，依据对知识网格的定义、模型、体系结构以及相关概念的研究，提出了基于知识网格的知识资源管理模式。希望此模式能为有效管理下一代Web信息、知识和服务资源提供相应的理论指导。     

网格环境下分布式元学习任务的设计

元学习方法是采用集成学习的方式来生成最终的全局预测模型。该方法的基本思想是从已经获得的知识中再进行学习,从而得到最终的数据模式。网格能有效地为元学习提供高性能和分布式的基础设施。文中根据知识网格的概念,在Globus Toolkit的基础上,分析了知识网格的体系结构和它的主要组件。根据分布式元学习的一般过程,设计了在知识网格体系结构下的元学习任务。     

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.     

Service-oriented middleware for distributed data mining on the grid

Distribution of data and computation allows for solving larger problems and executing applications that are distributed in nature. The grid is a distributed computing infrastructure that enables coordinated resource sharing within dynamic organizations consisting of individuals, institutions, and resources. The grid extends the distributed and parallel computing paradigms allowing for resource negotiation and dynamical allocation, heterogeneity, open protocols, and services. Grid environments can be used both for compute-intensive tasks and data intensive applications by exploiting their resources, services, and data access mechanisms. Data mining algorithms and knowledge discovery processes are both compute and data intensive, therefore the grid can offer a computing and data management infrastructure for supporting decentralized and parallel data analysis. This paper discusses how grid computing can be used to support distributed data mining. Research activities in grid-based data mining and some challenges in this area are presented along with some promising future directions for developing grid-based distributed data mining.     

基于网格的知识发现

该文在介绍网格知识发现概念的基础上,提出了网格知识发现架构设计的基本原则和组件,设计了一种新型的网格知识发现框架,并在此架构上分析了集中式数据挖掘和分布式数据挖掘的全过程,最后给出了工作展望。     

Distributed data mining on grids: services, tools, and applications

Data mining algorithms are widely used today for the analysis of large corporate and scientific datasets stored in databases and data archives. Industry, science, and commerce fields often need to analyze very large datasets maintained over geographically distributed sites by using the computational power of distributed and parallel systems. The grid can play a significant role in providing an effective computational support for distributed knowledge discovery applications. For the development of data mining applications on grids we designed a system called Knowledge Grid. This paper describes the Knowledge Grid framework and presents the toolset provided by the Knowledge Grid for implementing distributed knowledge discovery. The paper discusses how to design and implement data mining applications by using the Knowledge Grid tools starting from searching grid resources, composing software and data components, and executing the resulting data mining process on a grid. Some performance results are also discussed.     

数据挖掘在网格中的应用

网格计算是分布计算的一个新的重要的分支,它主要是实现了大规模资源的共享,并且达到了高性能。在许多应用中,需要对大量的数据集进行分析,而这些数据通常是地理上分布的大规模的数据,并且复杂度不断在增加。对于以上的这些应用,网格技术提供了有效的支持,介绍了网格的基础设施以及分布式数据挖掘。     

Metadata for Managing Grid Resources in Data Mining Applications

The Grid is an infrastructure for resource sharing and coordinated use of those resources in dynamic heterogeneous distributed environments. The effective use of a Grid requires the definition of metadata for managing the heterogeneity of involved resources that include computers, data, network facilities, and software tools provided by different organizations. Metadata management becomes a key issue when complex applications, such as data-intensive simulations and data mining applications, are executed on a Grid. This paper discusses metadata models for heterogeneous resource management in Grid-based data mining applications. In particular, it discusses how resources are represented and managed in the Knowledge Grid, a framework for Grid-enabled distributed data mining. The paper illustrates how XML-based metadata is used to describe data mining tools, data sources, mining models, and execution plans, and how metadata is used for the design and execution of distributed knowledge discovery applications on Grids.