Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
Mining call and mobility data to improve paging efficiency in cellular networks
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
Traffic Forecasting Based on Chaos Analysis in GSM Communication Network
CISW '07 Proceedings of the 2007 International Conference on Computational Intelligence and Security Workshops
Predicting future locations using prediction-by-partial-match
Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments
Active GSM cell-id tracking: "Where Did You Disappear?"
Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments
On prediction using variable order Markov models
Journal of Artificial Intelligence Research
Motion-Based adaptation of information services for mobile users
UM'05 Proceedings of the 10th international conference on User Modeling
Mobility detection using everyday GSM traces
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
The role of prediction algorithms in the MavHome smart home architecture
IEEE Wireless Communications
Performance study of active tracking in a cellular network using a modular signaling platform
Proceedings of the 8th international conference on Mobile systems, applications, and services
Identifying important places in people's lives from cellular network data
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
Human mobility modeling at metropolitan scales
Proceedings of the 10th international conference on Mobile systems, applications, and services
Active tracking in mobile networks: An in-depth view
Computer Networks: The International Journal of Computer and Telecommunications Networking
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We consider the problem of predicting user location in the form of user-cell association in a cellular wireless network. This is motivated by resource optimization, for example switching base transceiver stations on or off to save on network energy consumption. We use GSM traces obtained from an operator, and compare several prediction methods. First, we find that, on our trace data, user cell sector association can be correctly predicted in ca. 80% of the cases. Second, we propose a new method, called "MARPL", which uses Market Basket Analysis to separate patterns where prediction by partial match (PPM) works well from those where repetition of the last known location (LAST) is best. Third, we propose that for network resource optimization, predicting the aggregate location of a user ensemble may be of more interest than separate predictions for all users; this motivates us to develop soft prediction methods, where the prediction is a spatial probability distribution rather than the most likely location. Last, we compare soft predictions methods to a classical time and space analysis (ISTAR). In terms of relative mean square error, MARPL with soft prediction and ISTAR perform better than all other methods, with a slight advantage to MARPL (but the numerical complexity of MARPL is much less than ISTAR).