Since the growth of internet is increasing day by day, hence the amount of data that is storing in Web Server is also increasing rapidly. The growth of number of users of internet is also increasing at a rapid rate, this in turn increasing the Web traffic, so we need some type of strategies or mechanism that can handle this rapid growth of Web traffic. Web Prefetching and caching are techniques that can be used to deal with this increased growth of Web Traffic. Web prefetching and caching are processes that prefetch frequent pages which are likely to be requested in near future and caching is used to store these pages in Proxy Cache Server. Here we have proposed some cache replacement policies by which the hit ratio is likely to get increased. We have proposed novel pre fetching and caching scheme to access frequent data items. It helps in improving pattern analysis, and pattern generation process. Proposed techniques will be useful in E-commerce, Web personalization for customer requirement & satisfaction. This will reduce the user overall access time in future.

World wide web, Web log, Web mining, Web usage mining, Web Transactions, Caching policies, LRU, LFU, Web Prefetching, Web caching, Hit Ratio

[1] H.T. Chen, Pre-fetching and Re-fetching in Web caching systems: Algorithms and Simulation, Master Thesis, TRENT UNIVESITY, Peterborough, Ontario, Canada(2008).
[2] 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.
[3] T. Koskela, J. Heikkonen, ,and K. Kaski, (2003). “Web cache optimization with nonlinear model using object feature”, Computer Networks journal, elsevier , 43(6), ( 2003), pp. 805-817.
[4] J. Cobb, and H. Elaarag, “Web proxy cache replacement scheme based on back-propagation neural network”, Journal of System and Software, 81(9), (2008), pp. 1539-1558.
[5] R. Ayani, Y.M. Teo, and Y.S. Ng, “Cache pollution in Web proxy servers”, International Parallel and Distributed Processing Symposium (IPDPS`03), 22-26 April 2003, pp.7.
[6] A.K.Y. Wong, ” Web Cache Replacement Policies: A Pragmatic Approach”, IEEE Network magazine, 20(1), (2006), pp.28–34.
[7] I. R. Chiang, P. B.Goes, and Z. Zhang, “Periodic cache replacement policy for dynamic content at application server”, Decision Support Systems, Elsevier, 43 (2), (2007), pp. 336- 348.
[8] H.k. Lee, B.S. An, and E.J. Kim, “Adaptive Prefetching Scheme Using Web Log Mining in Cluster-Based Web Systems”, 2009 IEEE International Conference on Web Services (ICWS), (2009), pp.903-910.
[9] L. Jianhui, X. Tianshu, Y. Chao. “Research on WEB Cache Prediction Recommend Mechanism Based on Usage Pattern”, First International Workshop on Knowledge Discovery and Data Mining(WKDD), (2008), pp.473-476.
[10] A. Abhari, S. P. Dandamudi, and S.Majumdar , ”Web Object-Based Storage Management in Proxy Caches”, Future Generation Computer Systems Journal , 22(1-2), (2006). pp. 16-33.
[11] 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.
[12]. Koskela, J. Heikkonen, ,and K. Kaski, (2003). "Web cache optimization with nonlinear model using object feature", Computer Networks journal, elsevier , 43(6), ( 2003), pp. 805-817
[13] Peter Pirolli, James Pitkow, and Ramana Rao. Silk from a sow`s ear: Extracting usable structures from the web. In CHI-96, Vancouver, 1996.
[14] 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.
[15] 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.
[16] M.S.Chen, J. Hart, and P.S. Yu. Data mining: An overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering, 8(6):866- 883, 1996.
[17] Rabinovich M, Spatsheck O. Web caching and replication. Addison Wesley; 2002.
[18] T. M. Kroeger, D. D. E. Long, and J. C. Mogul, “Exploring the bounds of web latency reduction from caching and prefetching”, Proceedings of the USENDC Symposium on Internet Technology and Systems, (1997), pp. 13-22.
[19] U. Acharjee, Personalized and Artificial Intelligence Web Caching and Prefetching. Master thesis, University of Ottawa,Canada(2006). A Survey of Web Caching and Prefetching.
[20] Y.f. Huang and J.M. Hsu, “Mining web logs to improve hit ratios of prefetching and caching”. Knowledge-Based Systems, 21(1), (2008), pp. 62- 69.
[21] G. Pallis, A. Vakali, and J.Pokorny, “A clustering-based prefetching scheme on a Web cache environment”, Computers and Electrical Engineering, 34(4), (2008). pp.309-323.
[22] W. Feng, S. Man, and G.Hu, “Markov Tree Prediction on Web Cache Prefetching”, Software Engineering, Artificial Intelligence(SCI), Springer- Verlag Berlin Heidelberg, 209,(2009). pp. 105–120.
[23] Robert Cooley, Bamshad Mobasher, and Jaideep Srivastava. Data preparation for mining world wide web browsing patterns. Knowledge and Information Systems, 1(1), 1999.
[24] Zhang T., Ramakrishnan R., and Livny M., “Birch: AnEfficient Data Clustering Method for Very Large Databases.” In Proceedings of the ACM SIGMODConference on Management of Data, pages 103-114, Montreal, Canada, June 1996.
[25] Schloegel K, Karypis G, Kumar V. Parallel multilevel algorithms for multi-constraint graph partitioning. In: Proceedings of 6th international Euro-Par conference. September 2000. p. 296– 310.
[26] G. Bejerano, “Algorithms for Variable Length Markov Chain Modelling, Bioinformatics, vol. 20, pp. 788-789, Mar. 2004.
[27] J. Borges and M. Levene. “Evaluating Variable Length Markov Chain Models for Analysis of User Web Navigation Sessions”, IEEE Transactions on Knowledge and Data Engineering,19 (4), pp. 441-452, April 2007.
[28] Nanhay Singh, Arvind Panwar and Ram Shringar Raw. Enhancing the performance of Web Proxy Server using Cluster Based Pre-fetching technique. IEEE 2013.
[29] Study of Web Pre-Fetching With Web Caching Based On Machine Learning Technique " (K R Baskaran, Dr. C.Kalarasan, A Sasi Nachimuthu) (2013).
[30] Hybrid Approach for Performance of WebPage Response through Web UsageMining”(Ravinder Singh,Bhumika garg) (2014).