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

TL;DR

This paper proposes a dynamic web page prediction model that clusters pages by access patterns and uses page ranking to improve user latency, addressing limitations of static prefetching techniques.

Contribution

It introduces a novel methodology combining clustering and page ranking to enhance dynamic page prediction for better user experience.

Findings

Effective clustering of related pages based on access patterns.

Use of page ranking improves initial prediction accuracy.

Reduces need for large log databases in prefetching models.

Abstract

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