Containerization of model fitting workloads over spatial datasets
Date
2021
Authors
Warushavithana, Menuka, author
Pallickara, Shrideep, advisor
Pallickara, Sangmi, advisor
Breidt, Jay, committee member
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Abstract
Spatial data volumes have grown exponentially over the past several years. The number of domains in which spatial data are extensively leveraged include atmospheric sciences, environmental monitoring, ecological modeling, epidemiology, sociology, commerce, and social media among others. These data are often used to understand phenomena and inform decision making by fitting models to them. In this study, we present our methodology to fit models at scale over spatial data. Our methodology encompasses segmentation, spatial similarity based on the dataset(s) under consideration, and transfer learning schemes that are informed by the spatial similarity to train models faster while utilizing fewer resources. We consider several model fitting algorithms and execution within containerized environments as we profile the suitability of our methodology. Our benchmarks validate the suitability of our methodology to facilitate faster, resource-efficient training of models over spatial data.
Description
Rights Access
Subject
model fitting tasks
spatial data
resource management
containers