基于结构相关性Markov模型的Web网页预取方法

预取技术通过在用户浏览当前网页的时间内提前取回其将来最有可能请求的网页来减小实际感知的获取网页的时间。预测的准确性和方法的可用性是预取技术需要解决的主要问题。针对目前Web网页预取的一般方法的不足之处,提出了一种基于结构相关性Markov模型的Web网页预取方法。仿真实验的结果表明,这种方法在保证一定预测准确性的同时也具有较好的可用性,能够在减小用户访问延迟、提高响应速度方面达到较为满意的效果。

Prefetching Web Pages by Using a Structural-Relation-based Markov Model

Prefetching can reduce the retrieval time perceived by users by predicting and fetching the most likely Web pages that are to be requested soon,while the user is browsing through the current displayed page.The precision of predicting and applicability of the method are the major problems needed to be resolved.To avoid the faults of current general ones,this article presents a new prefetching method which uses the structural-relation-based Markov model.Results of stimulation experiment show that this method has excellent applicability as well as certain precision,and can acquire satisfactory effects in reducing latency and improving response speed.