Learning routines over long-term sensor data using topic models


Recent advances on sensor network technology provide the infrastructure to create intelligent environments on physical places. One of the main issues of sensor networks is the large amount of data they generate. Therefore, it is necessary to have good data analysis techniques with the aim of learning and discovering what is happening on the monitored environment. The problem becomes even more challenging if this process is performed following an unsupervised way (without having any a priori information) and applied over a long-term timeline with many sensors. In this work, topic models are employed to learn the latent structure and dynamics of sensor network data. Experimental results using two realistic datasets, having over 50 weeks of data, have shown the ability to find routines of activity over sensor network data in office environments.