Offering Memory Efficiency Utilizing Cellular Automata for Markov Tree Based Web-Page Prediction Model

In this paper, an approach for storing Markov tree, used in various versions of PPM model while predicting next Web-page is proposed. Markov tree requires huge amount of memory. This problem is solved using cellular automata which is considered as a fast and inexpensive mechanism. The proposed technique utilizes non-linear single cycle multiple attractor cellular automata (SMACA) which replaces Markov tree for minimizing the memory requirement.