Rough Set Granularity in Mobile Web Pre-caching

Mobile Web pre-caching (Web prefetching and caching) is an explication of performance enhancement and storage limitation of mobile devices. In this paper, we present the granularity of rough sets (RS) and RS based inductive learning in reducing the size of rules to be stored in mobile devices. The conditional attributes such as ´timestamp´, ´size document´ and ´object retrieval time´ are presented and the provided decision is granted. Decision rules are obtained using RS based inductive learning to grant the ultimate judgment either to cache or not to cache the conditional attributes and objects. However, in mobile clients, these rules need to be specified so as the specific sessions can be kept in their mobile storage as well as the proxy caching administrations. Consequently, these rules will notify the objects in Web application request and priority level to the clients accordingly. The results represent that the granularity of RS in mobile Web pre-caching is vital to improve the latest Web caching technology by providing virtual client and administrator feedback; hence making Web pre-caching technologies practical, efficient and powerful.