Abstract
Wi-Fi hot spots have quickly increased in recent years. Accordingly, discovering user positions by using Wi-Fi fingerprints has attracted much research attention. Wi-Fi fingerprints are the sets of Wi-Fi scanning results recorded in mobile devices. However, the issue of data uncertainty is not considered in the proposed Wi-Fi positioning systems. In this paper, we propose a framework to find user trajectories from the Wi-Fi fingerprints recorded in the smartphones. In this framework, we first discover meaningful places with the proposed Wi-Fi distance metric. Second, we propose two similarity functions to recognize the places and show the probabilities of the places where a user stayed in by the proposed uncertain data models. Finally, an algorithm on probabilistic sequential pattern mining is used for finding user trajectories. A series of experiments are performed to evaluate each step of the framework. The experiment results reveal that each step of our framework is with high accuracy.
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© 2016 Springer International Publishing Switzerland
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Chen, Y.C., Wang, E.T., Chen, A.L.P. (2016). Mining User Trajectories from Smartphone Data Considering Data Uncertainty. In: Madria, S., Hara, T. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2016. Lecture Notes in Computer Science(), vol 9829. Springer, Cham. https://doi.org/10.1007/978-3-319-43946-4_4
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DOI: https://doi.org/10.1007/978-3-319-43946-4_4
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