We propose a model of the situational context of a person and show how it can be used to organize and, consequently, reason about massive streams of sensor data and annotations, as they can be collected from mobile devices, e.g. smartphones, smartwatches or fitness trackers. The proposed model is validated on a very large dataset about the everyday life of one hundred and fifty-eight people over four weeks, twenty-four hours a day.
A Context Model for Personal Data Streams / Giunchiglia, Fausto; Li, Xiaoyue; Busso, Matteo; Marcelo, Rodas-Britez. - 13421:(2023), pp. 37-44. (Intervento presentato al convegno APWeb-WAIM 2022 tenutosi a 南京, Nanjing, China, nel 25–27 November 2022) [10.1007/978-3-031-25158-0_4].
A Context Model for Personal Data Streams
Fausto Giunchiglia;Xiaoyue Li
;Matteo Busso;Marcelo Rodas-Britez
2023-01-01
Abstract
We propose a model of the situational context of a person and show how it can be used to organize and, consequently, reason about massive streams of sensor data and annotations, as they can be collected from mobile devices, e.g. smartphones, smartwatches or fitness trackers. The proposed model is validated on a very large dataset about the everyday life of one hundred and fifty-eight people over four weeks, twenty-four hours a day.| File | Dimensione | Formato | |
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2022_APWEB___Context_model.pdf
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