  The multitude of data generated by sensors available on users' mobile
devices, combined with advances in machine learning techniques, support
context-aware services in recognizing the current situation of a user (i.e.,
physical context) and optimizing the system's personalization features.
However, context-awareness performances mainly depend on the accuracy of the
context inference process, which is strictly tied to the availability of
large-scale and labeled datasets. In this work, we present a framework
developed to collect datasets containing heterogeneous sensing data derived
from personal mobile devices. The framework has been used by 3 voluntary users
for two weeks, generating a dataset with more than 36K samples and 1331
features. We also propose a lightweight approach to model the user context able
to efficiently perform the entire reasoning process on the user mobile device.
To this aim, we used six dimensionality reduction techniques in order to
optimize the context classification. Experimental results on the generated
dataset show that we achieve a 10x speed up and a feature reduction of more
than 90% while keeping the accuracy loss less than 3%.
