Use Case

A Cloud-Based Framework for Visualization and Analysis of Big Geospatial Data

V. Chandola (University at Buffalo) with R. Vatsavai (North Carolina State University), P. Hogan (NASA Ames Research Center), B. B. Bhaduri (Oak Ridge National Laboratory)


  • Integration, visualization, and analysis of geospatial data from around the world

Why Aristotle?

  • Offloading of variable computational demands from local infrastructure
  • Expert service hub for widely distributed data
  • Scalable and elastic computing


  • Developed the capabilities to create Spark analytical clusters on-demand.
  • Deployed a new distributed machine learning method for change detection in sustainability data.
  • Ran scalability tests on Red Cloud at Cornell before migrating the application to Lake Effect cloud at the University at Buffalo.
  • Developed webGlobe, a browser-based, cloud-driven interactive 3D user interface that allows scientists to upload, visualize, and analyze Network Common Data Form (NetCDF) data sets and is, at present, the only browser-based system available with this functionality.
  • Completed initial runs of Gaussian Process-based change detection algorithm on 200 years of climate simulation data.
  • Refined webGlobe’s integrated visualization and analysis capabilities that support a variety of data formats prevalent in the climate and earth science communities.
  • Continued development of an Energy-Water Knowledge Discovery Framework portal using webGlobe technology currently running on the Aristotle cloud.
  • Performed comparative evaluations of various machine learning methods on Aristotle resources to better understand the Energy-Water nexus.
  • Shipped a version of webGlobe to collaborators at Oak Ridge National Laboratory to support their research activities in the area of climate data analytics.


  • Scale up climate simulation data analyses and run across federated sites.
  • Package the webGlobe software into an open source toolkit library that will be released for public use to enable a broad scientific community dealing with geo problems to launch analytical workflows and study massive spatial and spatio-temporal datasets through machine learning-driven analytics and advanced visualization.
  • Include two open-source and extensible core libraries in the toolkit to allow scientists to create integrated or stand-alone workflows for analytics and visualization and provide an integrated browser-based system to allow scientists to drive the analytics and visualization of geo data from a single platform.


Disparate geospatial data sources will be integrated with iGlobe and Aristotle
Disparate geospatial data sources will be integrated with webGlobe and Aristotle

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