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Keywords = Active LeZi

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Article
Study of LZ-Based Location Prediction and Its Application to Transportation Recommender Systems
by Alicia Rodriguez-Carrion, Carlos Garcia-Rubio, Celeste Campo, Alberto Cortés-Martín, Estrella Garcia-Lozano and Patricia Noriega-Vivas
Sensors 2012, 12(6), 7496-7517; https://doi.org/10.3390/s120607496 - 4 Jun 2012
Cited by 22 | Viewed by 8845
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 [...] Read more.
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. Full article
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Graphical abstract

Graphical abstract
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<p>LZ tree after parsing the example movement history <span class="html-italic">L</span> = <span class="html-italic">abababcdcbdab</span>.</p>
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<p>Combinations of the two independent stages.</p>
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<p>Comparison of hit rate attained when fixing the tree updating scheme and varying the probability calculation method.</p>
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<p>Hit rate evolution when processing the 4 days trace with Active LeZi updating scheme combined with each probability calculation method.</p>
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<p>ALZ tree after parsing the example movement history.</p>
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<p>Comparison of hit rate attained when fixing the probability calculation method and varying the tree updating scheme.</p>
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<p>Node count of different trees (log scale).</p>
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<p>Accumulated processing time needed by Active LeZi updating scheme combined with each probability calculation method (log scale).</p>
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<p>Processing time spent by a mobile phone for processing each new cell and estimating the most probable next location using Active LeZi and PPM without exclusion algorithm.</p>
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