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Open AccessArticle

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

1
Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
2
Robert Bosch, Corporate Sector Research and Advance Engineering, 70049 Stuttgart, Germany
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Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in PerCom 2018: Karatzoglou, A.; Köhler, D.; Beigl, M. Purpose-of-Visit-Driven Semantic Similarity Analysis on Semantic Trajectories for Enhancing the Future Location Prediction. In Proceedings of the 14th Workshop on Context and Activity Modeling and Recognition (CoMoRea’18), Athens, Greece, 19–23 March 2018.
Sensors 2018, 18(10), 3582; https://doi.org/10.3390/s18103582
Received: 1 September 2018 / Revised: 1 October 2018 / Accepted: 11 October 2018 / Published: 22 October 2018
(This article belongs to the Special Issue Context and Activity Modelling and Recognition)
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 capture the aforementioned dynamics, we define an entity, which we refer to as Purpose-of-Visit-Dependent Frame (PoVDF). In the third part of this work, we describe in detail the PoVDF-based approach and we evaluate it against the multi-dimensional Markov Chain model as well as with a semantic trajectory mining and prefix tree based model. Our evaluation shows that the PoVDF-based approach outperforms its competition and lays a solid foundation for further investigation. View Full-Text
Keywords: semantic trajectories; semantic location prediction; semantic similarity; multi-dimensional markov chains; context awareness semantic trajectories; semantic location prediction; semantic similarity; multi-dimensional markov chains; context awareness
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Karatzoglou, A.; Köhler, D.; Beigl, M. Semantic-Enhanced Multi-Dimensional Markov Chains on Semantic Trajectories for Predicting Future Locations. Sensors 2018, 18, 3582.

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