Towards human mobility extraction based on social media with Complex Event Processing

Social media has enabled a new breed of soft sensors that enriches the IoT paradigm with new forms of data. The present work introduces a novel approach for personal mobility mining that combines these new data-sources with built-in sensors of a smart-phone in order to timely extract personal mobility pattens by means of the Complex Event Processing (CEP) approach. Unlike previous solutions, the present work profits from both the textual and location data of social-network sites by also dealing with the actual scarcity of geo-tagged documents in those sites. Finally, a preliminary study of the feasibility of our proposal is stated.