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2011
 Rodriguez-Carrion, Alicia;  García-Rubio, Carlos;  Campo-Vázquez, Celeste;  Cortés, Alberto;  García-Lozano, Estrella;  Noriega-Vivas, Patricia
Analysis of Location Prediction Performance of LZ Algorithms using GSM Cell-based Location Data Conference 
5th International Symposium on Ubiquitous Computing and Ambient Intelligence (UCAMI 2011), UCAMI, 2011.
Abstract | Links | BibTeX | Tags: españavirtual, GSM, gsm-based location, lz, LZ Algorithms, prediction, predictions
@conference{campo024,

title = {Analysis of Location Prediction Performance of LZ Algorithms using GSM Cell-based Location Data},

author = {Alicia Rodriguez-Carrion and Carlos García-Rubio and Celeste Campo-Vázquez and Alberto Cortés and Estrella García-Lozano and Patricia Noriega-Vivas},

url = {https://hdl.handle.net/10016/13103

https://e-archivo.uc3m.es/bitstreams/66c1a251-8ce0-4a0f-b342-86a8b45e60e8/download},

year  = {2011},

date = {2011-12-20},

urldate = {2011-12-20},

booktitle = {5th International Symposium on Ubiquitous Computing and Ambient Intelligence (UCAMI 2011)},

publisher = {UCAMI},

abstract = {Predictions 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.},

keywords = {españavirtual, GSM, gsm-based location, lz, LZ Algorithms, prediction, predictions},

pubstate = {published},

tppubtype = {conference}

}

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Predictions 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.
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https://hdl.handle.net/10016/13103
https://e-archivo.uc3m.es/bitstreams/66c1a251-8ce0-4a0f-b342-86a8b45e60e8/downlo[...]
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