Study of LZ-Based Location Prediction and Its Application to Transportation Recommender Systems
Identifieur interne : 000838 ( Ncbi/Checkpoint ); précédent : 000837; suivant : 000839Study of LZ-Based Location Prediction and Its Application to Transportation Recommender Systems
Auteurs : Alicia Rodriguez-Carrion ; Carlos Garcia-Rubio ; Celeste Campo ; Alberto Cortés-Martín ; Estrella Garcia-Lozano ; Patricia Noriega-VivasSource :
- Sensors (Basel, Switzerland) [ 1424-8220 ] ; 2012.
Abstract
Predicting users' next location allows to anticipate their future context, thus providing additional time to be ready for that context and react consequently. This work is focused on a set of LZ-based algorithms (LZ, LeZi Update and Active LeZi) capable of learning mobility patterns and estimating the next location with low resource needs, which makes it possible to execute them on mobile devices. The original algorithms have been divided into two phases, thus being possible to mix them and check which combination is the best one to obtain better prediction accuracy or lower resource consumption. To make such comparisons, a set of GSM-based mobility traces of 95 different users is considered. Finally, a prototype for mobile devices that integrates the predictors in a public transportation recommender system is described in order to show an example of how to take advantage of location prediction in an ubiquitous computing environment.
Url:
DOI: 10.3390/s120607496
PubMed: 22969357
PubMed Central: 3435986
Affiliations:
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This work is focused on a set of LZ-based algorithms (LZ, LeZi Update and Active LeZi) capable of learning mobility patterns and estimating the next location with low resource needs, which makes it possible to execute them on mobile devices. The original algorithms have been divided into two phases, thus being possible to mix them and check which combination is the best one to obtain better prediction accuracy or lower resource consumption. To make such comparisons, a set of GSM-based mobility traces of 95 different users is considered. Finally, a prototype for mobile devices that integrates the predictors in a public transportation recommender system is described in order to show an example of how to take advantage of location prediction in an ubiquitous computing environment.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Cook, D J" uniqKey="Cook D">D.J. 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Lazer</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list></list><tree><noCountry><name sortKey="Campo, Celeste" sort="Campo, Celeste" uniqKey="Campo C" first="Celeste" last="Campo">Celeste Campo</name><name sortKey="Cortes Martin, Alberto" sort="Cortes Martin, Alberto" uniqKey="Cortes Martin A" first="Alberto" last="Cortés-Martín">Alberto Cortés-Martín</name><name sortKey="Garcia Lozano, Estrella" sort="Garcia Lozano, Estrella" uniqKey="Garcia Lozano E" first="Estrella" last="Garcia-Lozano">Estrella Garcia-Lozano</name><name sortKey="Garcia Rubio, Carlos" sort="Garcia Rubio, Carlos" uniqKey="Garcia Rubio C" first="Carlos" last="Garcia-Rubio">Carlos Garcia-Rubio</name><name sortKey="Noriega Vivas, Patricia" sort="Noriega Vivas, Patricia" uniqKey="Noriega Vivas P" first="Patricia" last="Noriega-Vivas">Patricia Noriega-Vivas</name><name sortKey="Rodriguez Carrion, Alicia" sort="Rodriguez Carrion, Alicia" uniqKey="Rodriguez Carrion A" first="Alicia" last="Rodriguez-Carrion">Alicia Rodriguez-Carrion</name></noCountry></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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