Unsupervised segmentation of human habits in smart home logs through process discovery

Smart homes represent examples of cyber-physical environments realizing the paradigm known as ambient intelligence. An information system supporting ambient intelligence takes as input raw sensor measurements and analyzes them to eventually make decisions following final user preferences and needs. Unfortunately, algorithms in this research area are mostly supervised, thus requiring a manual labeling of training instances usually involving final users in annoying and imprecise training sessions. In this paper, we propose an unsupervised approach allowing, given a sensor log, to automatically segment human habits on a temporal basis, by applying a bottom-up discretization strategy to the timestamp attribute of the sensor log.