Project: Usable Multi-Scale Big-Data Analytics through Interactive Visualization

Project Information:

• This material is based upon work supported by the National Science Foundation under Grant No. 1447416, entitled "BIGDATA: F: DKA: Usable Multiple-Scale Big-Data Analytics through Interactive Visualization", by PIs Chris North, Leanna House, Scotland Leman, and Wu Feng; project duration 9/2014 – 8/2018. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
• Project Overview (including Goals, Challenges, Intellectual Merits, and Broader Impacts)
• Project Personnel (including PIs and students) and Collaborators
• Publications (open access)
• Research results (with videos, presentations, demos, etc.):
  • SIRIUS: Two-view interaction with observations and attributes
  • Interactive clustering
  • Semantic Interaction Pipeline: software architecture multi-scale big data analytics
  • BigSpire: Semantic Interaction with Big Text Data on Bing & IEEE Explore
  • Andromeda: Semantic Interaction for Quantitative Data
  • GPU Based Methods for Interactive Visualization of Big Data
  • Visual Analytics with Biclusters
  • Smooth Interactive Visualization with Big Data
• Educational results: (new data analytics education modules for classes)
  • Be the Data: Embodied Visual Analytics for STEM education outreach
  • Educational modules presented at USCOTS 17, Pre-Conference Workshop, applied in CMDA 3654 Intro to Data Analytics and Visualization and CS 5764 Information Visualization
  • New education modules for Data and Decisions Destination Area and a new minor degree program hosted by CMDA.
• Point of Contact: PI Dr. Chris North