Proceedings of the 5th International Symposium of Ubiquitous Computing and Ambient Intelligence (UCAMI 2011), December 5-8th, 2011, Riviera Maya, MexicoPredictions about users' next locations allow
bringing forward their future context, thus having additional
time to react. To make such predictions, algorithms capable of
learning mobility patterns and estimating the next location are
needed. This work is focused on making the predictions on
mobile terminals, thus resource consumption being an important constraint. Among the predictors with low resource
consumption, the family of LZ algorithms has been chosen to study their performance, analyzing the results drawn from
processing location records of 95 users. The main contribution is to divide the algorithms into two phases, thus being possible to use the best combination to obtain better prediction accuracy or lower resource consumption.Proyecto CCG10-UC3M/TIC-4992 de la Comunidad Autónoma de Madrid y la Universidad Carlos III de Madri
