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Title |
A Framework for Predictive Web Prefetching at the Proxy Level using Data Mining |
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Author |
Jyoti Pandey, Amit Goel, Dr. A K Sharma |
| Citation |
Vol. 8 No. 6 pp. 303-308 |
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Abstract |
The intelligence of adapting to the needs of specific users has become a potential research area for web technologies amidst the problems like heterogeneous network connectivity, real-world distances, and congestion due to unexpected network demand. Web caching, one such adaptations, is a technology that helps to reduce the network usage and server loads and improve average latencies experienced by the user. Web latency would be further significantly reduced if proxy or Web server softwares could make predictions about the pages that a user is most likely to request next, while the user is viewing the current page, and prefetch their content. This technique is known as predictive web preftching. Both these techniques, along with the use of data mining mechanisms have been used in this paper for optimizing the access time for accessing web pages from a web server. In the proxy server, the proposed data mining based service would run in background mode. The service computes the web pages likely to be requested by the user, considering their past web access log history, using association rules and thus optimizing the access time. |
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Keywords |
Framework, Predictive Web Prefetch, Data Mining |
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