Linear clustering of objects with multiple attributes
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
A study of integrated prefetching and caching strategies
Proceedings of the 1995 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Using predictive prefetching to improve World Wide Web latency
ACM SIGCOMM Computer Communication Review
Link prediction and path analysis using Markov chains
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Integrated document caching and prefetching in storage hierarchies based on Markov-chain predictions
The VLDB Journal — The International Journal on Very Large Data Bases
The case for geographical push-caching
HOTOS '95 Proceedings of the Fifth Workshop on Hot Topics in Operating Systems (HotOS-V)
Efficient Query Result Retrieval over the Web
ICPADS '00 Proceedings of the Seventh International Conference on Parallel and Distributed Systems
The measured access characteristics of world-wide-web client proxy caches
USITS'97 Proceedings of the USENIX Symposium on Internet Technologies and Systems on USENIX Symposium on Internet Technologies and Systems
Alleviating the latency and bandwidth problems in WWW browsing
USITS'97 Proceedings of the USENIX Symposium on Internet Technologies and Systems on USENIX Symposium on Internet Technologies and Systems
| Hi-index | 0.00 |
The service speed of tiled-web data such as a map can be improved by prefetching future tiles while the current one is being displayed. Traditional prefetching techniques examine the transition probabilities among the tiles to predict the next tile to be requested. However, when the tile space is very huge, and a large portion of it is accessed with even distribution, it is very costly to monitor all those tiles. A technique that captures the regularity in the tile request pattern by using an NSMC (Neighbor Selection Markov Chain) has been suggested. The required regularity to use the technique is that the next tile to be requested is dependent on previous k movements (or requests) in the tile space. Maps show such regularity in a sense. Electronic books show a strong such regularity. The NSMC captures that regularity and predicts the client's next movement. However, Since the real-life movements are rarely strictly regular, we need to show that NSMC is robust enough such that with random movements occurred frequently, it still captures the regularity and predicts the future movement with a very high accuracy.