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Title: Unsupervised Learning Through Randomized Algorithms for High-Volume High-Velocity Data (ULTRA-HV).

Abstract

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.

Authors:
 [1];  [1];  [2];  [1];  [3]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Wake Forest Univ., Winston-Salem, MA (United States)
  3. Univ. of California, Berkeley, CA (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1417788
Report Number(s):
SAND-2018-0387R
659926
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Pinar, Ali, Kolda, Tamara G., Carlberg, Kevin Thomas, Ballard, Grey, and Mahoney, Michael. Unsupervised Learning Through Randomized Algorithms for High-Volume High-Velocity Data (ULTRA-HV).. United States: N. p., 2018. Web. doi:10.2172/1417788.
Pinar, Ali, Kolda, Tamara G., Carlberg, Kevin Thomas, Ballard, Grey, & Mahoney, Michael. Unsupervised Learning Through Randomized Algorithms for High-Volume High-Velocity Data (ULTRA-HV).. United States. doi:10.2172/1417788.
Pinar, Ali, Kolda, Tamara G., Carlberg, Kevin Thomas, Ballard, Grey, and Mahoney, Michael. Mon . "Unsupervised Learning Through Randomized Algorithms for High-Volume High-Velocity Data (ULTRA-HV).". United States. doi:10.2172/1417788. https://www.osti.gov/servlets/purl/1417788.
@article{osti_1417788,
title = {Unsupervised Learning Through Randomized Algorithms for High-Volume High-Velocity Data (ULTRA-HV).},
author = {Pinar, Ali and Kolda, Tamara G. and Carlberg, Kevin Thomas and Ballard, Grey and Mahoney, Michael},
abstractNote = {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.},
doi = {10.2172/1417788},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {1}
}

Technical Report:

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