Towards a mobile and context-aware framework from crowdsourced data

Capturing users' spatio-temporal context by recognizing their interests, locations, history and activities, and thereafter providing context-aware services is a challenging task. In this paper, we propose a spatio-temporal zoning model that takes different context dimensions into account and try to recommend necessary services to users in a personalized way. First, we propose a generic zoning model with unrestricted set of contexts where both spatial and temporal dimensions are relaxed, followed by two semi-restricted zoning models in which either spatial or temporal dimension is relaxed, while the other one is restricted. Finally, we show the model requiring restricted spatio-temporal zoning that applies to the scenario where millions of users need to perform some activities that have to be performed in a certain location and at a certain temporal period. We use the above zoning model for Hajj and Umrah events to define pilgrim's spatio-temporal contexts by capturing their real-time and historic activities through their smartphones' sensory data. This allows to intelligently recommend a set of necessary services to the users. We present a few of the implementations introduced in our proposed system.