An Architecture for Metadata-driven Integration of Heterogeneous Sensor and Health Data for Translational Exposomic Research

Exposomic research requires the generation of comprehensive spatio-temporal records of exposures along with capturing associated metadata describing limitations and uncertainties associated with the data. We describe the architecture of a metadata-driven Big Data integration platform for integration of sensor and health data to support diverse translational exposomic research.