Optimal prefetching via data compression
Journal of the ACM (JACM)
Analysis of branch prediction via data compression
Proceedings of the seventh international conference on Architectural support for programming languages and operating systems
Truth from trash: how learning makes sense
Truth from trash: how learning makes sense
Activity rhythm detection and modeling
CHI '03 Extended Abstracts on Human Factors in Computing Systems
Unbounded length contexts for PPM
DCC '95 Proceedings of the Conference on Data Compression
Learning Significant Locations and Predicting User Movement with GPS
ISWC '02 Proceedings of the 6th IEEE International Symposium on Wearable Computers
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
Rhythm modeling, visualizations and applications
Proceedings of the 16th annual ACM symposium on User interface software and technology
Selective Markov models for predicting Web page accesses
ACM Transactions on Internet Technology (TOIT)
On prediction using variable order Markov models
Journal of Artificial Intelligence Research
Place lab: device positioning using radio beacons in the wild
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
The role of prediction algorithms in the MavHome smart home architecture
IEEE Wireless Communications
Predicting user-cell association in cellular networks from tracked data
MELT'09 Proceedings of the 2nd international conference on Mobile entity localization and tracking in GPS-less environments
Location based information storage and dissemination in vehicular ad hoc networks
ADBIS'09 Proceedings of the 13th East European conference on Advances in Databases and Information Systems
A theoretical model for obfuscating web navigation trails
Proceedings of the Joint EDBT/ICDT 2013 Workshops
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We implemented the Prediction-by-Partial-Match data compression algorithm as a predictor of future locations. Positioning was done using IEEE 802.11 wireless access logs. Several experiments were run to determine how to divide the data for training and testing and how to best represent the data as a string of symbols. Our test data consisted of 198 datasets containing over 28,000 pairs, obtained from the UCSD Wireless Topology Discovery project. Tests of a first-order PPM model revealed a 90% success rate in predicting a user's location given the time. The third-order model, which is given the previous time and location and asked to predict the location at a given time, is correct 92% of the time.