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The growth of the World Wide Web has emphasized the need for improvement inuser latency. One of the techniques that are used for improving user latency isCaching and another is Web Prefetching. Approaches that bank solely on cachingoffer limited performance improvement because it is difficult for caching tohandle the large number of increasingly diverse files. Studies have beenconducted on prefetching models based on decision trees, Markov chains, andpath analysis. However, the increased uses of dynamic pages, frequent changesin site structure and user access patterns have limited the efficacy of thesestatic techniques. In this paper, we have proposed a methodology to clusterrelated pages into different categories based on the access patterns.Additionally we use page ranking to build up our prediction model at theinitial stages when users haven't already started sending requests. This way wehave tried to overcome the problems of maintaining huge databases which isneeded in case of log based techniques.

A Dynamic Web Page Prediction Model Based on Access Patterns to Offer Better User Latency