Application of Monte Carlo Search for Performance Improvement of Web Page Prediction

  • Authors

    • K Shyamala
    • S Kalaivani
    2018-06-25
    https://doi.org/10.14419/ijet.v7i3.4.16761
  • Hidden Markov Model, Monte Carlo Prediction, Prediction, Prefetch, Web Server Log

  • Abstract

    Prediction in web mining is one of the most complex tasks which will reduce web user latency. The main objective of this research work is to reduce web user latency by predicting and prefetching the users future request page. Web user activities were analyzed and monitored from the web server log file. The present work consists of two phases. In the first phase a directed graph is constructed for web user navigation with the reduction of repeated path. In the second phase, Monte Carlo search is applied on the constructed graph to predict the future request and prefetch the page. This work is successfully implemented and the prediction technique gives a better accuracy. This implementation paves a new way to prefetch the predicted pages at user end to reduce the user latency. Proposed Monte Carlo Prediction (MCP) Algorithm is compared with the existing algorithm Hidden Markov model. Proposed algorithm achieved better accuracy than the Hidden Markov Model. Accuracy is measured for the predicted web pages and achieved the optimal results.

     

     

  • References

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  • How to Cite

    Shyamala, K., & Kalaivani, S. (2018). Application of Monte Carlo Search for Performance Improvement of Web Page Prediction. International Journal of Engineering & Technology, 7(3.4), 133-137. https://doi.org/10.14419/ijet.v7i3.4.16761

    Received date: 2018-08-03

    Accepted date: 2018-08-03

    Published date: 2018-06-25