Large Consumer-Generated Data Optimization and Prediction

The proposed research aims to target large-scale consumer-generated data to analyze, visualize, and make predictions out of. The data will be collected from the consumers to make assessments on their lifestyles, and will come in forms such as heart-rate variance, that is, being temporal data. Researchers with visual analytics background will apply new visualization techniques on the data in order to grasp the insights and improve the model to interpret the data. The research problem is to relate measures of stress, recovery and mindful activities to the data obtained. The purpose of the proposed research is to complement the data with subjective measures such as happiness, life stress, and mindfulness. Some of the main objectives are to find out whether increased awareness of stress/recovery is associated with a change in stress/recovery; or whether increased daily mindful activity is associated with a change in stress/recovery. Expected results involve finding out whether the new product will contribute to a change in stress/recovery, or to increased mindful activity.

Faculty Supervisor:

Wolfgang Stuerzlinger


Gokhan Cetin


Lululemon Athletica



Information and communications technologies


Simon Fraser University



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