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Semantic-Enhanced Multi-Dimensional Markov Chains on Semantic Trajectories for Predicting Future Locations

Karatzoglou, Antonios; Köhler, Dominik; Beigl, Michael

Abstract:
In this work, we investigate the performance of Markov Chains with respect to modelling semantic trajectories and predicting future locations. In the first part, we examine whether and to what degree the semantic level of semantic trajectories affects the predictive performance of a spatial Markov model. It can be shown that the choice of the semantic level when describing trajectories has a significant impact on the accuracy of the models. High-level descriptions lead to better results than low-level ones. The second part introduces a multi-dimensional Markov Chain construct that considers, besides locations, additional context information, such as time, day and the users’ activity. While the respective approach is able to outperform our baseline, we could also identify some limitations. These are mainly attributed to its sensitivity towards small-sized training datasets. We attempt to overcome this issue, among others, by adding a semantic similarity analysis component to our model that takes the varying role of locations due each time to the respective purpose of visiting the particular location explicitly into consideration. To ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000087051
Veröffentlicht am 29.10.2018
Seitenaufrufe: 35
seit 02.11.2018
Downloads: 26
seit 02.11.2018
Zugehörige Institution(en) am KIT Institut für Telematik (TM)
Publikationstyp Zeitschriftenaufsatz
Jahr 2018
Sprache Englisch
Identifikator ISSN: 1424-8220
urn:nbn:de:swb:90-870519
KITopen-ID: 1000087051
Erschienen in Sensors
Band 18
Heft 10
Seiten 3582
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Vorab online veröffentlicht am 22.10.2018
Schlagworte semantic trajectories; semantic location prediction; semantic similarity; multi-dimensional markov chains; context awareness
Nachgewiesen in Web of Science
Scopus
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