数据挖掘技术在Web预取中的应用研究

Applying Data Mining to Web Pre-Fetching

WWW以其多媒体的传输及良好的交互性而倍受青睐 .虽然近几年来网络速度得到了很大的提高 ,但是由于接入 Internet的用户数量剧增以及 Web服务和网络固有的延迟 ,使得网络越来越拥挤 ,用户的服务质量得不到很好的保证 .为此文中提出了一种智能 Web预取技术 ,它能够加快用户浏览 Web页面时获取页面的速度 .该技术通过简化的 WWW数据模型表示用户浏览器缓冲器中的数据 ,在此基础上利用数据挖掘技术挖掘用户的兴趣关联规则 ,存放在兴趣关联知识库中 ,作为对用户行为进行预测的依据 .在用户端 ,智能代理负责用户兴趣的挖掘及基于兴趣关联知识库的 Web预取 ,从而对用户实现透明的浏览器加速 .

WWW is popular for its multimedia transmission and friendly interactivity. Although the speed of network has been improved considerably in recent years, the rapid expansion of using the Internet, the inherited character of delay in the network and the Request/Response working mode of WWW still make the Internet traffic very slow and give no guarantee on the Quality of Service. Because HTTP has no states, the web server cannot know the users' demand and the users' requests cannot be predicted. Taking advantage of a cache mechanism and the time locality of WWW accesses, the browser can preserve the documents ever accessed in the local machine. By this means, for the documents in the local cache, the browser does not need to send the requests to the remote server or to receive the whole responses from the remote one. Pre-fetching uses the space locality of accesses. First, the users' access requests are predicted according to the users' current request. Secondly, the expected pages are fetched into the local cache when the user is browsing the current page. Finally, the users can access these pages downloaded from the local cache. And this can reduce the access delay to some degrees. Pre-fetching is one kind of active caches that can cache the pages which are still not requested by the user. The application of pre-fetching technology in the web can greatly reduce the waiting time after users have sent their requests. This paper brings forward an intelligent technique of web pre-fetching, which can speed up fetching web pages. In this technique, we use a simplified WWW data model to represent the data in the cache of web browser to mine the association rules. We store these rules in a knowledge base so as to predict the user's actions. In the client sides, the agents are responsible for mining the users' interest and pre-fetching the web pages, which are based on the interest association repository. Therefore it is transparent for the users to speed up the browsing.