Practical prefetching via data compression
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Using predictive prefetching to improve World Wide Web latency
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Automated hoarding for mobile computers
Proceedings of the sixteenth ACM symposium on Operating systems principles
Using path profiles to predict HTTP requests
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Web prefetching between low-bandwidth clients and proxies: potential and performance
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Determining WWW User's Next Access and Its Application to Pre-fetching
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USITS'97 Proceedings of the USENIX Symposium on Internet Technologies and Systems on USENIX Symposium on Internet Technologies and Systems
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ATEC '97 Proceedings of the annual conference on USENIX Annual Technical Conference
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ATEC '99 Proceedings of the annual conference on USENIX Annual Technical Conference
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IEEE Internet Computing
An online PPM prediction model for web prefetching
Proceedings of the 9th annual ACM international workshop on Web information and data management
Online granular prediction model for web prefetching
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Information Systems Frontiers
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Expert Systems with Applications: An International Journal
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WISM'12 Proceedings of the 2012 international conference on Web Information Systems and Mining
Proceedings of the International Conference on Management of Emergent Digital EcoSystems
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International Journal of Information Technology and Web Engineering
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Abstract--Prefetching is an important technique to reduce the average Web access latency. Existing prefetching methods are based mostly on URL graphs. They use the graphical nature of HTTP links to determine the possible paths through a hypertext system. Although the URL graph-based approaches are effective in prefetching of frequently accessed documents, few of them can prefetch those URLs that are rarely visited. This paper presents a keyword-based semantic prefetching approach to overcome the limitation. It predicts future requests based on semantic preferences of past retrieved Web documents. We apply this technique to Internet news services and implement a client-side personalized prefetching system: NewsAgent. The system exploits semantic preferences by analyzing keywords in URL anchor text of previously accessed documents in different news categories. It employs a neural network model over the keyword set to predict future requests. The system features a self-learning capability and good adaptability to the change of client surfing interest. NewsAgent does not exploit keyword synonymy for conservativeness in prefetching. However, it alleviates the impact of keyword polysemy by taking into account server-provided categorical information in decision-making and, hence, captures more semantic knowledge than term-document literal matching methods. Experimental results from daily browsing of ABC News, CNN, and MSNBC news sites for a period of three months show an achievement of up to 60 percent hit ratio due to prefetching.