Optimal Prefetching in Random Trees

We propose and analyze a model for optimizing the prefetching of documents, in the situation where the connection between documents is discovered progressively. A random surfer moves along the edges of a random tree representing possible sequences of documents, which is known to a controller only up to depth d. A quantity k of documents can be prefetched between two movements. The question is to determine which nodes of the known tree should be prefetched so as to minimize the probability of the surfer moving to a node not prefetched. We analyzed the model with the tools of Markov decision process theory. We formally identified the optimal policy in several situations, and we identified it numerically in others.

IMPLEMENTATION OF PAGERANK ALGORITHM IN MATLAB

Implementation of the PageRank algorithm to rank web generally only contain static and dynamic pages, with the rapid url users then needed an algorithm for calculating web rankings. In determining the ranking of a web, links incoming and outgoing links are also random surfer model is one decisive factor in determining the ranking of a web. Implementation of PageRank on MATLAB formed on a program in the m-file.