Unsupervised Learning Through Randomized Algorithms for High-Volume High-Velocity Data (ULTRA-HV).
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States), Ali Pinar, USDOE National Nuclear Security Administration (NNSA), Tamara Kolda, Kevin Carlberg, Grey Ballard, Michael W. Mahoney · 2018
Through long-term investments in computing, algorithms, facilities, and instrumentation, DOE is an established leader in massive-scale, high-fidelity simulations, as well as science-leading experimentation. In both cases, DOE is generating more data than it can analyze and the problem is intensifying quickly. The need for advanced algorithms that can automatically convert the abundance of data into a wealth of useful information by discovering hidden structures is well recognized. Such efforts however, are hindered by the massive volume of the data and its high velocity. Here, the challenge is developing unsupervised learning methods to discover hidden structure in high-volume, high-velocity data.