  The widespread diffusion of connected smart devices has contributed to the
rapid expansion and evolution of the Internet at its edge. Personal mobile
devices interact with other smart objects in their surroundings, adapting
behavior based on rapidly changing user context. The ability of mobile devices
to process this data locally is crucial for quick adaptation. This can be
achieved through a single elaboration process integrated into user applications
or a middleware platform for context processing. However, the lack of public
datasets considering user context complexity in the mobile environment hinders
research progress. We introduce MyDigitalFootprint, a large-scale dataset
comprising smartphone sensor data, physical proximity information, and Online
Social Networks interactions. This dataset supports multimodal context
recognition and social relationship modeling. It spans two months of
measurements from 31 volunteer users in their natural environment, allowing for
unrestricted behavior. Existing public datasets focus on limited context data
for specific applications, while ours offers comprehensive information on the
user context in the mobile environment. To demonstrate the dataset's
effectiveness, we present three context-aware applications utilizing various
machine learning tasks: (i) a social link prediction algorithm based on
physical proximity data, (ii) daily-life activity recognition using
smartphone-embedded sensors data, and (iii) a pervasive context-aware
recommender system. Our dataset, with its heterogeneity of information, serves
as a valuable resource to validate new research in mobile and edge computing.
