基于网格计算框架的Web信息提取系统的研究

大规模Web信息提取是面向Internet非规范知识处理中的一个典型问题.以网格计算框架为实现平台,设计了分层的网格应用系统架构,针对Web信息提取中链接分析和信息提取功能,描述了面向一般网格计算框架的资源调度与编程模型.最后结合Web信息提取系统的实验结果,给出了网格应用系统的评价标准.     

使用GPDK实现网格门户的开发

开放网格服务体系结构(OGA)是基于网格和Web服务领域的概念和技术。门户是访问高性能计算机系统上的服务的用户界面。使用GPDK(Grid Portal Development Kit)可以方便地构造Web界面。本文给出了一个模拟复杂流体运动的实验网格系统。在该系统中，门户是在GPDK平台上设计的。     

一个应用于中文信息处理的网格系统的设计

介绍了目前中文信息处理研究的不足和网格技术的发展.为了扩大计算和存储的共享规模,并充分复用各类技术和研究,提出了将中文信息处理的应用利用网格技术来实现的方案.描述了一个应用于中文信息处理的网格系统的整体框架,该框架分为四层:顶部是一个Web门户,中间是中文信息处理网格管理器和网格中间件,最下面是网格节点.另外,还分析了负责资源管理和作业分配的CIPG管理器的设计方案.     

BUILDING A DATA-MINING GRID FOR MULTIPLE HUMAN BRAIN DATA ANALYSIS

E-science is about global collaboration in key areas of science such as cognitive science and brain science, and the next generation of infrastructure such as the Wisdom Web and Knowledge Grids. As a case study, we investigate human multiperception mechanism by cooperatively using various psychological experiments, physiological measurements, and data mining techniques for developing artificial systems which match human ability in specific aspects. In particular, we observe fMRI (functional magnetic resonance imaging) and EEG (electroencephalogram) brain activations from the viewpoint of peculiarity oriented mining and propose a way of peculiarity oriented mining for knowledge discovery in multiple human brain data. Based on such experience and needs, we concentrate on the architectural aspect of a brain-informatics portal from the perspective of the Wisdom Web and Knowledge Grids. We describe how to build a data-mining grid on the Wisdom Web for multiaspect human brain data analysis. The proposed methodology attempts to change the perspective of cognitive scientists from a single type of experimental data analysis toward a holistic view at a long-term, global field of vision.     

CUSTOMIZING THE INSTRUCTIONAL GRID

The Web contains hundreds of thousands of educational resources available any time and any place. However no smart technology is available to help teachers and students locate appropriate resources customized to their needs and social characteristics. When educational resources are indexed, it is often done by demographics, such as student age and grade. This article describes customized Grid Learning Services (GLS) that will personalize instruction based on an individual's presumed knowledge and cognitive and learning needs. The customized GLS will use real-time student modeling, the Semantic Web, intelligent agents, and pre-tests of cognitive, affective, and social characteristics to personalize the selection of educational resources and problems. Components of the customized GLS include an ontology construction agent, goal-based retrieval mechanisms, a lesson planner, and student and pedagogical agents.     

Programming Grid Applications with GRID Superscalar

The aim of GRID superscalar is to reduce the development complexity of Grid applications to the minimum, in such a way that writing an application for a computational Grid may be as easy as writing a sequential application. Our assumption is that Grid applications would be in a lot of cases composed of tasks, most of them repetitive. The granularity of these tasks will be of the level of simulations or programs, and the data objects will be files. GRID superscalar allows application developers to write their application in a sequential fashion. The requirements to run that sequential application in a computational Grid are the specification of the interface of the tasks that should be run in the Grid, and, at some points, calls to the GRID superscalar interface functions and link with the run-time library.GRID superscalar provides an underlying run-time that is able to detect the inherent parallelism of the sequential application and performs concurrent task submission. In addition to a data-dependence analysis based on those input/output task parameters which are files, techniques such as file renaming and file locality are applied to increase the application performance. This paper presents the current GRID superscalar prototype based on Globus Toolkit 2.x, together with examples and performance evaluation of some benchmarks.     

基于自适应微粒群算法的网格工作流调度

服务网格是一个通过组合网格服务为用户提供强有力的各种服务的系统,其中网格服务遵循OGSA的标准.网格服务工作流调度的关键在于如何在应用程序运行过程中能动态地根据当前系统中基本服务的情况,组合出满足用户需要的服务.提出了一种自适应微粒群优化算法用于服务感知的Web服务选择,其中引入了一个特殊的速度变异操作来增强空间搜索的有效性,并融合了遗传算法杂交与变异.它不仅能很好地满足组合服务的需求,而且能更有效地进行全局搜索.仿真试验显示对于具有全局Qos约束条件的Web服务选择在执行效率上自适应微粒群优化算法明显优于其它混合遗传算法(如种群多样性控制遗传算法).     

计算Web智能研究综述

计算Web智能是近年来提出的一个崭新的研究方向，它结合了计算智能和Web技术，致力于提高Interrlet和无线网络上电子商务等Web应用的智能化程度。首先分析了计算Web智能的研究背景，然后阐述了计算Web智能的概念和相关技术，概括了计算Web智能当前的主要研究内容和应用，最后展望了计算Web智能未来的研究方向及面临的挑战。     

基于WSRF的药物网格设计与实现

分析了原有的药物网格系统在可扩展性和互操作性方面存在的问题,借助于Web服务和网格的主流标准技术,设计了基于WSRF(Web Services Resource Framework)的药物网格分层结构.讨论了基于该结构Web服务的实现,最后通过实验对新的药物网格平台进行了测试和分析.     

语义网格：语义Web与网格计算的融合

本文对近几年Web服务、语义Web及网格计算等一些新兴的技术进行了简要的回顾，并对它们的背景、特点及相互关系进行了分析，在这基础上，介绍了一种新的网格发展趋势——语义网格，对它的背景、目标、体系结构及其知识层进行了详细描述并简要总结了当前语义网格的研完现状。