Metrics-based markov chains for web analytics

Today the Web Pages with static contents (e.g. on line newspapers, public portals, didactic pages, etc.) are very widespread by Information Technology. These pages are carriers of knowledge so it is important to study their use. The common web analytics tools lack useful metrics for an evaluation of the dissemination of knowledge via the web. In this paper we propose a Method (called Knowledge Page Markovian Algorithm) that uses the Markov Chains on the data obtained from web analytics tools to evaluate the use of the knowledge pages. By this original approach, it is possible to create new Metrics to assess each web page for the eventual reengineering of the site. Finally, an application of the Method is shown on a website for three University Courses.