Analysis of Markov model on different web Prefetching and caching schemes

The World Wide Web is growing rapidly in terms of number of users and number of web application. With this growth the response time of retrieving the web document is increasing. User's experience on the internet can be improved by minimizing user's web access latency. This can be done by predicting the next step taken by user towards the accessing of web page in advance, so that the predicted web page can be prefetched and cached. This prefetching and caching is useful for reducing departure of user from the website and improving the quality of service. In this paper three different schemes for web Prefetching and caching are proposed i.e. Prefetching only, Prefetching with Caching and Prefetching from Caching. Prediction of the next accessed web page for prefetching and caching is achieved by modeling the web log using Dynamic Nested Markov model. Dynamic Nested Markov model is analyzed on these three Prefetching and Caching schemes. Experiments have been conducted on real world data sets.

[1]  Mark Levene,et al.  Data Mining of User Navigation Patterns , 1999, WEBKDD.

[2]  Hua Wang,et al.  A Framework of Combining Markov Model With Association Rules for Predicting Web Page Accesses , 2006, AusDM.

[3]  Michalis Vazirgiannis,et al.  Web path recommendations based on page ranking and Markov models , 2005, WIDM '05.

[4]  José Luis Cabral de Moura Borges,et al.  A data mining model to capture user web navigation patterns , 2000 .

[5]  George Pallis,et al.  A clustering-based prefetching scheme on a Web cache environment , 2008, Comput. Electr. Eng..

[6]  Ali A. Ghorbani,et al.  The reconstruction of user sessions from a server log using improved time-oriented heuristics , 2004, Proceedings. Second Annual Conference on Communication Networks and Services Research, 2004..

[7]  Mark Levene,et al.  Generating Dynamic Higher-Order Markov Models in Web Usage Mining , 2005, PKDD.

[8]  Qiang Yang,et al.  Integrating Web Prefetching and Caching Using Prediction Models , 2002, World Wide Web.

[9]  B Nigam,et al.  Generating a New Model for Predicting the Next Accessed Web Page in Web Usage Mining , 2010, 2010 3rd International Conference on Emerging Trends in Engineering and Technology.

[10]  R. Venkatesan,et al.  Semantic Web Prefetching Scheme using Naïve Bayes Classifier , 2010, Int. J. Comput. Sci. Appl..

[11]  Przemyslaw Kazienko,et al.  Mining Indirect Association Rules for Web Recommendation , 2009, Int. J. Appl. Math. Comput. Sci..

[12]  Mohamed Jemni,et al.  Automatic Recommendations for E-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval , 2008, 2008 Eighth IEEE International Conference on Advanced Learning Technologies.

[13]  George Karypis,et al.  Selective Markov models for predicting Web page accesses , 2004, TOIT.

[14]  Mark Levene,et al.  Testing the Predictive Power of Variable History Web Usage , 2007, Soft Comput..

[15]  Haider A Ramadhan,et al.  A Heuristic Based Approach for Improving Website Link Structure and Navigation , 2009 .

[16]  Myra Spiliopoulou,et al.  Web Usage Analysis and User Profiling , 2002, Lecture Notes in Computer Science.

[17]  Mark Levene,et al.  Evaluating Variable-Length Markov Chain Models for Analysis of User Web Navigation Sessions , 2007, IEEE Transactions on Knowledge and Data Engineering.

[18]  Xin Chen,et al.  A Popularity-Based Prediction Model for Web Prefetching , 2003, Computer.

[19]  Tihamér Levendovszky,et al.  Marcov Models for Web Access Prediction , 2007 .