Spaceprint: a Mobility-based Fingerprinting Scheme for Public Spaces
  In this paper, we address the problem of how automated situation-awareness
can be achieved by learning real-world situations from ubiquitously generated
mobility data. Without semantic input about the time and space where situations
take place, this turns out to be a fundamental challenging problem.
Uncertainties also introduce technical challenges when data is generated in
irregular time intervals, being mixed with noise, and errors. Purely relying on
temporal patterns observable in mobility data, in this paper, we propose
Spaceprint, a fully automated algorithm for finding the repetitive pattern of
similar situations in spaces. We evaluate this technique by showing how the
latent variables describing the category, and the actual identity of a space
can be discovered from the extracted situation patterns. Doing so, we use
different real-world mobility datasets with data about the presence of mobile
entities in a variety of spaces. We also evaluate the performance of this
technique by showing its robustness against uncertainties.
