Using predictive prefetching to improve World Wide Web latency
ACM SIGCOMM Computer Communication Review
Mining web logs for prediction models in WWW caching and prefetching
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Web-Log Mining for Predictive Web Caching
IEEE Transactions on Knowledge and Data Engineering
WhatNext: A Prediction System for Web Requests using N-gram Sequence Models
WISE '00 Proceedings of the First International Conference on Web Information Systems Engineering (WISE'00)-Volume 1 - Volume 1
Foundations of Soft Case-Based Reasoning
Foundations of Soft Case-Based Reasoning
Mining longest repeating subsequences to predict world wide web surfing
USITS'99 Proceedings of the 2nd conference on USENIX Symposium on Internet Technologies and Systems - Volume 2
Reducing file system latency using a predictive approach
USTC'94 Proceedings of the USENIX Summer 1994 Technical Conference on USENIX Summer 1994 Technical Conference - Volume 1
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Prefetching web content by predicting users' web requests can reduce the response time of the web server and optimize the network traffic. The Markov model that is based on the conditional probability has been studied by many researchers for web access path prediction. The prediction accuracy rate can reach up to 60 to 70 percent high. However a drawback of this type of model is that as the length of the access path grows the chance of successful path matching will decrease and the model will become inapplicable. In order to preserving the applicability as well as improving the accuracy rate, we extend the model by introducing a similarity measure among access paths. Therefore, the matching process becomes less rigid and the model will be more applicable and robust to the change of the path length.