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Data Storage Alternatives For A Gridded Crop Disease Risk Forecasting System, Paul J. Roehsner

Masters Theses

Three separate storage technologies able to serve gridded data were selected for comparison of performance in terms of providing speed and expandability to a crop disease forecasting system. The three storage technologies chosen were PostgreSQL (a relational database management system), MongoDB (NoSQL system), and netCDF files. Speed tests were performed for each by running two different crop disease risk forecasting models requiring data of different spatiotemporal resolutions. Multiple trials were done using different storage hardware. Systems were then qualitatively compared for expandability by noting the process involved in adding successive crop disease forecasting models.

It was found that due to …