文中提出了基于Multi-MobileAgent技术的个性化数据挖掘模型，介绍了该模型各个组成部分的功能，以及多个Agent之间如何相互协作完成个性化的数据挖掘，并给出了相关的算法。

rincipal component analysis(PCA) by neural network is an adaptive statistical signal processing method which has many applications Since many PCA neural networks do not converge globally, it is nature to study the global convergence of PCA learning algorithm. In this paper, the previous works on the globally convergent PCA neural networks are presented first. Then based on the mismatch of previous PCA neural networks, we propose and analyze a PCA learning algorithm. This algorithm is convergent globally. And a rigorous mathematical proof is given. Simulation results show the efiiciency and effectiveness ofthis algorithm.

讨论并比较了Internet数据库访问的两层和三层两种模式，描述了实现Internet数据库的CGI，API，JDBC三种方式。通过理论分析和数据测试，说明了在对Internet数据库访问性能上，JDBC方式优于CGI方式，API方式优于JDBC方式。

在总结传统软件开发方法经验的基础上，严格遵守软件开发方法原则，从UML建模思想出发，对传统UML三层抽象建模结构进行了分析，并引入“抽象”和“分解”作为UML建模的核心思想；提出了UML分层抽象建模机制的构想，将计算机系统开发过程概括为对功能信元、结构信元、关联信元、实现信元和集成信元的提取过程，并以此作为UML建模的指导，有效了利用UML完整语义定义，克服了传统面向对象开发方法的缺陷，实现了建模过程从需求到分析的过渡以及功能和实现事实上的分离。

In this paper, a system architecture of data mining in network based on the technology of Mobile Agent and RDF is presented. This system architecture provides a standard and uniform structured data-platform for the con-crete data-mining application. Mobile Agent implements the concrete data mining application. The relative technolo-gies of this system architecture are introduced first, then two species of logic architecture of the network based appli-cation system are explained. And an accomplished prototyping of network-based data mining system architecture is proposed. The combination of the up-to-date XML/RDF and Mobile Agent technologies provides a new method for data mining in network.