Scaling up to address data science challenges
Statistics and Data Science provide a variety of perspectives and technical approaches for exploring and understanding Big Data. Partnerships between scientists from different fields such as statistics, machine learning, computer science, and applied mathematics can lead to innovative approaches for addressing problems involving increasingly large amounts of data in a rigorous and effective manner that takes advantage of advances in computing. Here, this article will explore various challenges in Data Science and will highlight statistical approaches that can facilitate analysis of large-scale data including sampling and data reduction methods, techniques for effective analysis and visualization of large-scale simulations, and algorithms and procedures for efficient processing.
- Research Organization:
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- Grant/Contract Number:
- AC52-06NA25396
- OSTI ID:
- 1358169
- Report Number(s):
- LA-UR-16-29638
- Journal Information:
- Chance, Vol. 30, Issue 2; ISSN 0933-2480
- Publisher:
- SpringerCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Effective and efficient data sampling using bitmap indices
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journal | March 2014 |
In-situ Sampling of a Large-Scale Particle Simulation for Interactive Visualization and Analysis
|
journal | June 2011 |
Partitioning a Large Simulation as It Runs
|
journal | July 2016 |
Randomized selection on the GPU
|
conference | January 2011 |
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