Practical prefetching via data compression
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
The power of amnesia: learning probabilistic automata with variable memory length
Machine Learning - Special issue on COLT '94
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Optimal Prediction for Prefetching in the Worst Case
SIAM Journal on Computing
LeZi-update: an information-theoretic framework for personal mobility tracking in PCS networks
Wireless Networks - Selected Papers from Mobicom'99
A Data Mining Algorithm for Generalized Web Prefetching
IEEE Transactions on Knowledge and Data Engineering
Prediction suffix trees for supervised classification of sequences
Pattern Recognition Letters
Selective Markov models for predicting Web page accesses
ACM Transactions on Internet Technology (TOIT)
Web Caching in Broadcast Mobile Wireless Environments
IEEE Internet Computing
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
On prediction using variable order Markov models
Journal of Artificial Intelligence Research
The role of prediction algorithms in the MavHome smart home architecture
IEEE Wireless Communications
IEEE Wireless Communications
The context-tree weighting method: extensions
IEEE Transactions on Information Theory
The context-tree weighting method: basic properties
IEEE Transactions on Information Theory
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Discrete sequence modeling and prediction is a fundamental goal and a challenge for location-aware computing. Mobile client’s data request forecasting and location tracking in wireless cellular networks are characteristic application areas of sequence prediction in pervasive computing, where learning of sequential data could boost the underlying network’s performance. Approaches inspired from information theory comprise ideal solutions to the above problems, because several overheads in the mobile computing paradigm can be attributed to the randomness or uncertainty in a mobile client’s movement or data access. This article presents a new information-theoretic technique for discrete sequence prediction. It surveys the state-of-the-art solutions and provides a qualitative description of their strengths and weaknesses. Based on this analysis it proposes a new method, for which the preliminary experimental results exhibit its efficiency and robustness.