Caching Dynamic Web Documents using Similarity Profiles
Faculty of Computing, Health and Science
School of Computer and Security Science
Discovering similarities between web users and establishing similarities among web documents are two important research streams in web usage mining community in the recent years. The knowledge obtained from the exercise can be used for many applications such as predicting user’s preference, optimizing web cache organization and improving the quality of web document pre-fetching. This paper presents an approach of caching dynamic web documents using similarity profile, which is built based on the similarities among cached web documents. An offline phase of building the similarity profiles and online update of the similarity profiles are described to maintain the similarity profile in a similarity-aware multi-cache web caching architecture. An agent-based web document pre-fetching mechanism that references both the similarity profiles and similarities among web users is also presented to support the similarity-aware web caching architecture.