A Context Model for Personal Data Streams

We propose a model of the situational context person and show how it can be used to organize and, consequently, reason about massive streams sensor data annotations, as they collected from mobile devices, e.g. smartphones, smartwatches or fitness trackers. The proposed is validated on very large dataset everyday life one hundred fifty-eight people over four weeks, twenty-four hours day.