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Rapid Data Analytics Platform using Machine Learning
Completed
TRL 3 (started at 1, targeting 3)
Description
NASA's collection of observational and experimental data has undergone a revolutionary change in recent years. The ever-expanding breadth and fidelity of these scientific datasets have allowed NASA scientists to tackle real-world physical problems in unprecedented ways. While the high-fidelity data generated at NASA is a valuable resource for research and scientific exploration, it also presents a number of challenges, the most significant of which is the sheer volume and complexity of the data, which includes information from sources such as satellites, telescopes, spacecraft, and numerical simulations. In this proposal, we introduce a cutting-edge data analytics platform that uses a novel dimensional reduction algorithm to promote optimal use of NASA's growing collection of scientific data. Preliminary results show that when applied to a 2.2TB turbulent flow dataset our platform's novel algorithms yield a 60-1000x reduction in download and storage costs and a 10-1000x reduction in computational costs, depending on the desired accuracy in the solution.
Benefits
NASA generates vast amounts of data through its missions, and it needs advanced analytics tools and techniques to manage and analyze this data. Some NASA missions and applications requiring extensive analytics of scientific data include the Mars Curiosity mission, the NASA Solar Dynamics Observatory and the Webb Space Telescope (58GB/day), and the numerous earth science and climate modeling missions. The huge amount of CFD simulation data generated at NASA is also a prime candidate for optimized data analytics using our platform.
The target data for this platform is simulation data extracted from high fidelity numerical simulations and scientific data extracted from high profile experiments. Some of the many sectors developing such data include finance, transport, energy. automotive, and healthcare. Example companies and organizations include Lockheed, SNL and AFRL.
Details
| Technology area | Software, Modeling, Simulation, and Information Processing > Information Processing and Artificial Intelligence > Intelligent Data Understanding |
| Program | Small Business Innovation Research/Small Business Tech Transfer (SBIR/STTR) |
| Lead organization | RNET Technologies, Inc., Dayton, OH |
| Start date | 2023-08-03 |
| End date | 2024-02-02 |
Project contacts
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How to get involved
This is early/mid-stage (TRL 3) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.
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