A prediction system for multimedia pre-fetching in Internet

The rapid development of Internet has resulted in more and more multimedia in Web content. However, due to the limitation in the bandwidth and huge size of the multimedia data, users always suffer from long time waiting. On the other hand, if we can predict the web object or page that the user most likely will view next while the user is viewing the current page, and pre-fetch the content, then the perceived network latency can be significantly reduced. In this paper, we present an n-gram based model to utilize path profiles of users from very large web log to predict the users’ future requests. Our model is based on a simple extension of existing point-based models for such predictions, but our results show that by sacrificing the applicability somewhat one can gain a great deal in prediction precision. Also we present an efficient method to compress the prediction model size so that it can be fitted into the main memory. Our result can potentially be applied to a wide range of applications on the web, including pre-fetching, enhancement of recommendation systems as well as web caching policies. The experiments based on three realistic web logs have proved the effectiveness of the proposed scheme.