Social and Behavioral Sciences Commons^™

Automatic Scaling Hadoop In The Cloud For Efficient Process Of Big Geospatial Data, Zhenlong Li, Chaoweikai Yang, Kai Liu, Fei Hu, Baoxuan Jin

Faculty Publications

Efficient processing of big geospatial data is crucial for tackling global and regional challenges such as climate change and natural disasters, but it is challenging not only due to the massive data volume but also due to the intrinsic complexity and high dimensions of the geospatial datasets. While traditional computing infrastructure does not scale well with the rapidly increasing data volume, Hadoop has attracted increasing attention in geoscience communities for handling big geospatial data. Recently, many studies were carried out to investigate adopting Hadoop for processing big geospatial data, but how to adjust the computing resources to efficiently handle the …