Containerization of Model Fitting Workloads over Spatial Datasets

Containerization of Model Fitting Workloads over Spatial Datasets

연구 분야: Software Development

학회: 2021 IEEE International Conference on Big Data (Big Data)

Spatial data volumes have grown exponentially over the past several years. The number of domains that 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.