| Autor: |
Shaban-Nejad A; University of Tennessee Health Science Center-Oak Ridge National Laboratory (UTHSC-ORNL) Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, Memphis TN, USA., Brenas JH; Big Data Institute - Nuffield Department of Medicine, University of Oxford, Oxford OX3 7LF, U.K., Al Manir MS; Public Health Sciences, University of Virginia, Charlottesville, VA, USA., Zinszer K; School of Public Health, University of Montreal, Montréal, Québec, Canada., Baker CJO; Department of Computer Science, University of New Brunswick, Saint John, New Brunswick, Canada.; IPSNP Computing Inc., Canada. |
| Abstrakt: |
This paper reports on the early-stage development of an analytics framework to support the semantic integration of dynamic surveillance data across multiple scales to inform decision making for malaria eradication. We propose using the Semantic Web of Things (SWoT), a combination of Internet of Things (IoT) and semantic web technologies, to support the evolution and integration of dynamic malaria data sources and improve interoperability between different datasets generated through relevant IoT assets (e.g. computers, sensors, persons, and other smart objects and devices). |
