Abstract

According to the Centers for Disease Control, in the United States there are 6.8 million children living with asthma. Despite the importance of the disease, the available prognostic tools are not sufficient for biomedical researchers to thoroughly investigate the potential risks of the disease at scale. To overcome these challenges we present a big data integration and analysis infrastructure developed by our Data and Software Coordination and Integration Center (DSCIC) of the NIBIB-funded Pediatric Research using Integrated Sensor Monitoring Systems (PRISMS) program. Our goal is to help biomedical researchers to efficiently predict and prevent asthma attacks. The PRISMS-DSCIC is responsible for collecting, integrating, storing, and analyzing realtime environmental, physiological and behavioral data obtained from heterogeneous sensor and traditional data sources. Our architecture is based on the Apache …

Date

April 19, 2017

Authors

Dimitris Stripelis, José Luis Ambite, Yao-Yi Chiang, Sandrah P Eckel, Rima Habre

Conference

2017 IEEE 33rd International Conference on Data Engineering (ICDE)

Pages

1407-1408

Publisher

IEEE