A popularity-based prediction model for Web prefetching

The diverse server, client, and unique file object types used today slow Web performance. Caching alone offers limited performance relief because it cannot handle many different file types easily. One solution combines caching with Web prefetching: obtaining the Web data a client might need from data about that client´s past surfing activity. The prediction by partial match model, for example, makes prefetching decisions by reviewing URLs clients have accessed on a particular server, then structuring them in a Markov predictor tree. The authors propose a variation of this model that builds common surfing patterns and regularities into the tree.