Pipelining user trajectory analysis and visual process maps for habit mining

Models of human habits in smart spaces can be expressed by using a multitude of formalisms whose readability influences the possibility of being validated by human experts. In this paper we present a visual analysis pipeline that allows, starting from the sensor log of a smart space, to graphically visualize human habits. The intuition here is to apply techniques borrowed from the area of business process automation and mining on a version of the sensor log preprocessed in order to translate raw sensor measurements into human actions. The paper also presents some hints of how the proposed method can be employed to automatically extract models to be reused for ambient intelligence.