Simulated Annealing with estimated temperature
AI Communications - Special issue on AI research in the Benelux
Prefetch policies for large objects in a web-enabled GIS application
Data & Knowledge Engineering
Adaptation of a Neighbor Selection Markov Chain for Prefetching Tiled Web GIS Data
ADVIS '02 Proceedings of the Second International Conference on Advances in Information Systems
Information Sciences: an International Journal
Web-Based Access to Distributed High-Performance Geographic Information Systems for Decision Support
HICSS '99 Proceedings of the Thirty-second Annual Hawaii International Conference on System Sciences-Volume 6 - Volume 6
A Bayesian framework for automated dataset retrieval in Geographic Information Systems
MMM '04 Proceedings of the 10th International Multimedia Modelling Conference
Design Strategies to Improve Performance of GIS Web Services
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Improving prediction level of prefetching for location-aware mobile information service
Future Generation Computer Systems - Special issue: Modeling and simulation in supercomputing and telecommunications
Type-Level access pattern view: a technique for enhancing prefetching performance
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
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A major task of a Web GIS (Geographic Information Systems) system is to transfer map data to client applications over the Internet, which may be too costly. To improve this inefficient process, various solutions are available. Caching the responses of the requests on the client side is the most commonly implemented solution. However, this method may not be adequate by itself. Besides caching the responses, predicting the next possible requests from a client and updating the cache with responses for those requests together provide a remarkable performance improvement. This procedure is called "prefetching" and makes caching mechanisms more effective and efficient. This paper proposes an efficient prefetching algorithm called Retrospective Adaptive Prefetch (RAP), which is constructed over a heuristic method that considers the former actions of a given user. The algorithm reduces the user-perceived response time and improves user navigation efficiency. Additionally, it adjusts the cache size automatically, based on the memory size of the client's machine. RAP is compared with four other prefetching algorithms. The experiments show that RAP provides better performance enhancements than the other methods.