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We propose a context aware framework that offers a set of cloud-based services to support a very large Hajj and Umrah crowd by capturing their contexts using smartphones. The proposed framework captures the individual's context, provides a set of adapted services, and allows being in touch with a subset of one's community of interest. We leverage the spatiotemporal sensory data captured by our framework to define users' contexts for optimized services. Our proposed framework is also envisioned to assist the Hajj and Umrah authorities to (1) improve Hajj & Umrah documentation, (2) improve Hajj organization through better understanding of pilgrims' (individual and crowd) spatial and temporal behavior and needs, and (3) protect pilgrims' environment through environmental monitoring. In particular, the developed methods, techniques, and algorithms will support the pilgrimage quality of experience. We have tested our system through end-user subjects and due to apply for the upcoming Hajj events. We present our implementation details and the general impression of end users about our system.

With the advancement of mobile technologies, more and more people are connected to social networks such as Facebook and Twitter. Social networks allow users to share diversity of information including spatio-temporal data either publicly or within their community of interest in realtime. Particularly, by analyzing social network data streams and then validating the content, one can extract knowledge about dynamic road conditions for a given city. This paper presents a dynamic path recommender system that helps users finding optimized routes in dynamic environments based on social network data. The system collects geo-tagged social network data from which relevant knowledge is extracted for identifying constraints such as accidents, weather conditions, and congestions. Moreover, by continuously collecting moving user's geo-tagged data, the system can also identify the traffic flow as well as roads' conditions. As soon as the system identifies and validates a given constraint, it can notify affected users and recommend an adapted route from their current position to the destination. A proof of concept of the system will be shown through three example scenarios.

Due to the low cost and high availability of wearable health sensors and motion tracking devices, home based therapy monitoring has come to a reality. In this paper, we propose a gesture controlled e-therapy online framework that can monitor physical and occupational therapy exercises using multimedia data produced by different sensors such as Kinect2, Leap, and Myo. The multimedia therapeutic data is then stored in a big data repository with proper annotation. We have developed analytics to mine therapeutic information from the big data platform, such as finding the most appropriate therapy regime for a patient, based on her age, ethnicity, gender, disability level and geo-spatial location. We will show key queries that can be answered by our developed analytics.