SHF:Medium:Collaborative Research: Scalable Algorithms for Spatio-temporal Data Analysis

Project: Research project


Acceleration of computing power of supercomputers along with development and deployment of large instruments such as telescopes, colliders, sensors and devices raises one fundamental question. "Are we reducing time to insight and knowledge discovery at the same exponential rate?" The answer currently is clearly "NO", because a critical step, that of analytics, mining and discovering knowledge from the massive datasets has lagged far behind advances in software, simulation and generation of data. Analysis of data requires "data-driven" computing and analytics. This entails scalable software for data reduction, approximations, analysis, statistics, and bottom-up discovery. There is a clear need for developing scalable and sustainable analytics software if we are to make a significant leap forward in scientific discoveries. Furthermore, there is an obvious need to engage and leverage a broader community to accelerate the development and innovation of scalable analytics software, while achieving sustainability for this important area.

Keywords: High-performance data analysis
Effective start/end date6/1/145/31/19


  • National Science Foundation (CCF-1409601)


Colliding beam accelerators
Data mining
Sustainable development
Data reduction