  Modern mobile devices are able to provide context-aware and personalized
services to the users, by leveraging on their sensing capabilities to infer the
activity and situation in which a person is currently involved. Current
solutions for context-recognition rely on annotated data and expertsâ knowledge
to predict the user context. In addition, their prediction ability is strongly
limited to the set of situations considered during the model training or
definition. However, in a mobile environment, the user context continuously
evolves, and it cannot be merely restricted to a set of predefined classes. To
overcome these limitations, we propose COMPASS, a novel unsupervised and online
clustering algorithm aimed at identifying the user context in mobile
environments based on the stream of high-dimensional data generated by
smartphone sensors. COMPASScan distinguish an arbitrary number of userâs
contexts from the sensorsâ data, without defining a priori the collection of
expected situations. This key feature makes it a general-purpose solution to
provide context-aware features to mobile devices, supporting a broad set of
applications. Experimental results on 18 synthetic and 2 real-world datasets
show that COMPASS correctly identifies the user context from the sensorsâ data
stream, and outperforms the state-of-the-art solutions in terms of both
clusters configuration and purity. Eventually, we evaluate its performances in
terms of execution time and the results show that COMPASS can process 1000
high-dimensional samples in less than 20 seconds, while the reference solutions
require about 60 minutes to evaluate the entire dataset. Keywords:
Context-awareness, Unsupervised Machine Learning, Online Clustering, Mobile
Computing
