Workflow technology make software is modified quickly follow the requirement.It provides good flexibility for a software system.Software component technology realizes the reusage by encapsulating certain functions,and is regard as a key factor to realize the software reusage.Two technologies is presented based on which bring forward a new method to develop system using workflow business components,and the advantage and shortage of this method is analyzed and a prototype system is developed.

Web Services constitute a new computing model for Web application. The application based on SOA is a promising trend of distributed computing. The key and most difficult problem in SOA is how to automatically discover the services according to the end users’ query accurately and quickly. A new P2P and Semantic Web based service discovery mechanism is discussed in this paper in which the deployment and publication of a Web Service are bound together. When web service manager deploys his services, the embedded toolkit will create web service description files automatically and put them into the service metadata repository of the Peer. Profiting from using P2P network to exchange the metadata, the service providers are permitted to add and modify and delete services freely without conferring others. When a query is submitted by a service consumer, a two steps querying and two layers searching methods are used to improve the performance. Key words based matching and indexing in the Group layer are used during the first step, and semantic based Goal Capability matching in the Peer layer is used during the second step of querying. Two metrics, service growth time and service death time are also introduced in this paper to evaluate the discovery performance.

随着Internet的发展,电子文档的数量成指数级增长,大量的文档之间存在密切的联系。将这些电子文档发布到WWW上需要有效地建立这些大量文档之间的链接,从而为用户提供一个更加友好的导航界面。对于以超文本形式产生出来的大量文档,用手工的方式为其指定超链接,不但需要领域知识,而且将是一项极为繁重的劳动。因此,实现超文本建立的自动化是一项很有意义的工作。目前的各种超链建立方法存在着自动化程度不高和准确率低的缺点。本文基于关键词自动抽取提出了一种为文档自动建立超链接的方法。实验证明该方法取得了较好的效果。

Atomicity and anonymity are two important attributes for electronic commerce, especially in payment systems. Atomicity guarantees justice and coincidence for each participant. However traditional atomicity either bias to SELLER or CUSTOMER. Anonymity is also intractable, unanonymous or anonymity both leads to dissatisfying ending. Therefore, it is important to design a system in which satisfying atomicity and revocable anonymity are both enabled. In this paper, based on AFAP(Atomic and Fair Anonymous Protocol), we propose an approach to realize satisfying atomicity. In this method, not only SELLER’s satisfying atomicity but also CUSTOMER’s satisfying atomicity is supported. At the same time, based on Brands’ fair signature model, this method satisfies both anonymity and owner-trace or money-trace.

Keywords provide rich semantic information for documents. It benefits many applications such as topic retrieval, document clustering, etc. However, there still exist a large amount documents without keywords. Manually assigning keywords to existing documents is very laborious. Therefore it is highly desirable to automate the process. Traditional methods are mainly based on a predefined controlled-vocabulary, which is limited by unknown words. This paper presents a new approach based on Bayesian decision theory. The approach casts keyword distillation to a problem of loss minimization. To determine which word can be assigned as keywords becomes a problem to estimate the loss. Feature selection is one of the most important issues in machine learning. Several plausible attributes are always be assigned as the learning features, but they are all based on the assumption of words’ independence. Machine learning based on them dose not produce satisfactory results. In this paper, taking the word’ context and linkages between words into account, we extend the work of feature selection. Experiments show that our approach significantly improves the quality of extracted keywords.

Word sense and word dependency benefit many applications. Manually constructed lexicon usually serves as a source for word sense and word dependency. However, there always requires very expensive work and extensive time consumption to compile it, simultaneously senses and relationships among words in these kinds of lexica are always missed. Automatically compiled lexica are under developing, the main obstacle is to discover multisenses and multidependencies among words. We propose a new method combining an improved ISODATA clustering algorithm with association rule mining to answer the question. With the recursively clustering algorithm lower frequency senses are discovered. As well a approach for refinement is put forward to improve the precision. Experiments indicate that the approach presented here provides preferable outputs.

In order to study the role of metadata in distance education, builds a metadata model query model that is very simple and very general. The model's implementation is based on LDAP directory server, and provides fast and convenient querying service through web. The model is independent of the any e-learning application. Based on it, the further research on modern distance education is available.

Mobile agent is a new distributed computing model. In this paper,we discuss the concepts, characteristics, compare with tradition technology hurdle, application related to mobile agent,and attempts to preset an account of current research efforts.

文章提出了基于Agent的分布式网上高校招生管理系统模型.该模型由用户接口Agent、任务Agent和资源Agent三部分组成.文中阐述了各Agent的功能,建立了任务Agent的动态选择机制,描述了Agent间通讯方法.基于Agent的分布式管理系统模型已用于高校招生管理系统中,它提高了网上高考管理系统的性能,解决了分布式环境下从不同资源中获取所需信息的能力.