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Tell Me What Air You Breath, I Tell You Where You Are

Abstract : Wide spread use of sensors and mobile devices along with the new paradigm of Mobile Crowd-Sensing (MCS), allows monitoring air pollution in urban areas. Several measurements are collected, such as Particulate Matters, Nitrogen dioxide, and others. Mining the context of MCS data in such domains is a key factor for identifying the individuals' exposure to air pollution, but it is challenging due to the lack or the weakness of predictors. We have previously developed a multi-view learning approach which learns the context solely from the sensor measurements. In this demonstration, we propose a visualization tool (COMIC) showing the different recognized contexts using an improved version of our algorithm. We also demonstrate the change points detected by a multi-dimensional CPD model. We leverage real data from a MCS campaign, and compare different methods.
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https://hal.uvsq.fr/hal-03336989
Contributor : Équipe Hal Uvsq <>
Submitted on : Tuesday, September 7, 2021 - 2:51:00 PM
Last modification on : Friday, September 17, 2021 - 6:40:07 PM

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Distributed under a Creative Commons Attribution 4.0 International License

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Hafsa El Hafyani, Mohammad Abboud, Jingwei Zuo, Karine Zeitouni, Yehia Taher. Tell Me What Air You Breath, I Tell You Where You Are. SSTD '21: 17th International Symposium on Spatial and Temporal Databases, Aug 2021, virtual USA, France. pp.161-165, ⟨10.1145/3469830.3470914⟩. ⟨hal-03336989⟩

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