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Quantitative Study of Markov Model for Prediction of User Behavior for Web Caching and Prefetching Purpose

by Dharmendra T. Patel, Kalpesh Parikh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 65 - Number 15
Year of Publication: 2013
Authors: Dharmendra T. Patel, Kalpesh Parikh
10.5120/11004-6195

Dharmendra T. Patel, Kalpesh Parikh . Quantitative Study of Markov Model for Prediction of User Behavior for Web Caching and Prefetching Purpose. International Journal of Computer Applications. 65, 15 ( March 2013), 39-49. DOI=10.5120/11004-6195

@article{ 10.5120/11004-6195,
author = { Dharmendra T. Patel, Kalpesh Parikh },
title = { Quantitative Study of Markov Model for Prediction of User Behavior for Web Caching and Prefetching Purpose },
journal = { International Journal of Computer Applications },
issue_date = { March 2013 },
volume = { 65 },
number = { 15 },
month = { March },
year = { 2013 },
issn = { 0975-8887 },
pages = { 39-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume65/number15/11004-6195/ },
doi = { 10.5120/11004-6195 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:18:56.983878+05:30
%A Dharmendra T. Patel
%A Kalpesh Parikh
%T Quantitative Study of Markov Model for Prediction of User Behavior for Web Caching and Prefetching Purpose
%J International Journal of Computer Applications
%@ 0975-8887
%V 65
%N 15
%P 39-49
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In modern era every organization depends on internet to conduct business and as a result of that many hidden data are available in several log files of servers; which could serve many purposes in business and that give the birth of web mining field. Web Mining could useful for many applications in business but this paper focuses on web caching and prefetching application to reduce latency while accessing internet. The common problem in organization is; in spite of sufficient internet bandwidth; sometimes users feel delay while accessing several pages. The problem could be solved out by developing predictive model based on web caching and prefetching criteria and many research have been done using Markov based predictive model to reduce access latency while using internet. This paper focuses on quantitative study of Markov based predictive model for web caching and prefetching to determine limitations of Markov Model on prediction perspectives.

References
  1. Cooley, R; Mobasher, B. ; Srivastava, J, "Web mining: information and pattern discovery on the World Wide Web " , Tools with Artificial Intelligence, 1997. Proceedings. , Ninth IEEE International Conference, Pages-558-567.
  2. Ramakrishna, M. T. ; Gowdar, L. K. ; Havanur, M. S. ; Swamy, B. P. M, "Web Mining: Key Accomplishments, Applications and Future Directions" Data Storage and Data Engineering (DSDE), 2010 International IEEE Conference, Pages- 187 – 191.
  3. Ming-Syan Chen; Jiawei Han; Philip S. Yu, " Data Mining : An Overview from a Database Perspectives", IEE Transactions on knowledge and data engineering, Vol-8, December-1996, Pages-866-883.
  4. Zhang Haiyang, "The Research of Web Mining in E-Commerce", Management and Service Science (MASS), 2011 IEE International Conference, Pages-1-4.
  5. Raymand Kosala; Hendril Blokeel, " Web Mining Research : A Survey", ACM SIGKDD Explorations, July 2000, Vol-2, Issue-1, Pages-1-4.
  6. H. T. Chen, "Pre-fetching and Re-fetching in Web caching systems: Algorithms and Simulation" Master Thesis, TRENT UNIVESITY, Peterborough, Ontario, Canada(2008)
  7. T. Chen, "Obtaining the optimal cache document replacement policy for the caching system of an EC Website", European Journal of Operational Research. 181 ( 2),(2007), pp. 828. Amsterdam.
  8. U. Acharjee, Personalized and Artificial Intelligence Web Caching and Prefetching. Master thesis, University of Ottawa,Canada(2006).
  9. B. Zhijie, G. Zhimin, and J. Yu, "A Survey of Web Prefetching", Journal of computer research and development, 46(2), (2009), pp. 202-210.
  10. J. Domenech, J. A. Gil, J. Sahuquillo, and A. Pont, "Using current web page structure to improve prefetching performance", Computer Network Journal, 54(9), (2010), 1404-1417.
  11. Technet Library,Microsoft Products,Tools, Technologies ( www. technet. microsoft. com)
  12. K. R. Suneetha; Dr. R. Krishnamoorthi, " Identifying User Behavior by Analyzing Web Server Access Log File", IJCSNS International Journal of computer science and Network Security, Vol. 9,April 2009, pages-327-332.
  13. Nigam, B. , "Analysis of Markov model on different web Prefetching and caching schemes", Computational Intelligence and Computing Research (ICCIC), 2010 IEEE International Conference, Pages-1-6.
  14. Lei Shi; Yan Zhang ; Wei Lin, " Optimal Model of Web Caching and Prefetching", Proceeding of second Symposium International computer science and computational technology, China, Dec-2009,Pages-250-253.
  15. Wei-Guang Teng;Cheng-Yue Chang,;and Ming-Syan Chen, "Integrating Web Caching and Web Prefetching in Client-Side Proxies", IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 16, NO. 5, MAY 2005, Pages-444-455.
  16. W. Ali, and S. M. Shamsuddin, "Intelligent Client-side Web Caching Scheme Based on Least recently Used Algorithm and Neuro-Fuzzy System", The sixth International Symposium on Neural Networks(ISNN 2009), Lecture Notes in Computer Science (LNCS), Springer-Verlag Berlin Heidelberg , 5552, (2009), pp. 70–79.
  17. W. Tian, B. Choi, and V. V. Phoha,"An Adaptive Web Cache Access Predictor Using Neural Network". Proceedings of the 15th international conference on Industrial and engineering applications of artificial intelligence and expert systems: developments in applied artificial intelligence, Lecture Notes In Computer Science(LNCS), Springer- Verlag London, UK 2358, (2002). 450-459.
  18. A. P. Foong, Y. -H. Hu, and D. M. Heisey, "Logistic regression in an adaptive web cache", IEEE Internet Computing, 3, (1999), 27-36.
  19. H. ElAarag and S. Romano, "Improvement of the neural network proxy cache replacement strategy", Proceedings of the 2009 Spring Simulation Multiconference,(SSM'09), San Diego, California, (2009), pp: 90.
  20. Younghyun Kim ; Sangheon Pack ; Chung Gu Kang ; Soonjoon Park , "Exploiting spatial and temporal locality for seamless vertical handover" ,IEEE Communications and Information Technology, ISCIT 2009, Pages-1078 – 1083.
  21. X. Chen and X. Zhang, "Popularity-based PPM: An effective web prefetching technique for high accuracy and low storage", In Proceedings of the International Conference on Parallel Processing, (2002), pp. 296-304.
  22. Ali Bayir ,Smart Miner: A New Framework for Mining Large Scale Web Usage Data, Murat,Department of Computer,Science and Engineering,University at Buffalo, USA.
  23. E. P. Markatos and C. E. Chronaki , "A Top-10 approach to prefetching on the Web ", Proceedings of INET'98 Geneva, Switzerland, (1998), pp. 276-290.
  24. Y. Jiang, M. Y Wu, and W. Shu, "Web prefetching : Costs , benefits and performance", Proceedings of the 11th International World Wide Web Conference, New York, ACM, (2002).
  25. Dharmendra Patel, Dr. Kalpesh Parikh, Atul Patel, " Sessionization –A Vital Stage in Data Preprocessing of Web Usage Mining-A Survey", International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www. ijera. com Vol. 2, Issue 1, Jan-Feb. 2012, pp. 327-330.
Index Terms

Computer Science
Information Sciences

Keywords

Markov Model Web Mining Web Caching Web Prefetching Access Latency

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