  This paper investigates the Cyber-Physical behavior of users in a large
indoor shopping mall by leveraging anonymized (opt in) Wi-Fi association and
browsing logs recorded by the mall operators. Our analysis shows that many
users exhibit a high correlation between their cyber activities and their
physical context. To find this correlation, we propose a mechanism to
semantically label a physical space with rich categorical information from
DBPedia concepts and compute a contextual similarity that represents a user's
activities with the mall context. We demonstrate the application of
cyber-physical contextual similarity in two situations: user visit intent
classification and future location prediction. The experimental results
demonstrate that exploitation of contextual similarity significantly improves
the accuracy of such applications.
