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Sensors 2019, 19(3), 448; https://doi.org/10.3390/s19030448

Indexing Multivariate Mobile Data through Spatio-Temporal Event Detection and Clustering

1
Department of Computer Science, University of Rochester, Rochester, NY 14620, USA
2
School of Information Technology and Electrical Engineering, University of Queensland, Brisbane 4067, Australia
3
Department of Computer Science, University College London, London WC1E 6BT, UK
4
Department of Computer Science, University of California, Riverside, CA 92507, USA
*
Author to whom correspondence should be addressed.
Received: 12 December 2018 / Revised: 18 January 2019 / Accepted: 18 January 2019 / Published: 22 January 2019
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Abstract

Mobile and wearable devices are capable of quantifying user behaviors based on their contextual sensor data. However, few indexing and annotation mechanisms are available, due to difficulties inherent in raw multivariate data types and the relative sparsity of sensor data. These issues have slowed the development of higher level human-centric searching and querying mechanisms. Here, we propose a pipeline of three algorithms. First, we introduce a spatio-temporal event detection algorithm. Then, we introduce a clustering algorithm based on mobile contextual data. Our spatio-temporal clustering approach can be used as an annotation on raw sensor data. It improves information retrieval by reducing the search space and is based on searching only the related clusters. To further improve behavior quantification, the third algorithm identifies contrasting events within a cluster content. Two large real-world smartphone datasets have been used to evaluate our algorithms and demonstrate the utility and resource efficiency of our approach to search. View Full-Text
Keywords: spatio-temporal; clustering; event detection; mobile sensing: contrast behavior mining; human behavior spatio-temporal; clustering; event detection; mobile sensing: contrast behavior mining; human behavior
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Rawassizadeh, R.; Dobbins, C.; Akbari, M.; Pazzani, M. Indexing Multivariate Mobile Data through Spatio-Temporal Event Detection and Clustering. Sensors 2019, 19, 448.

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