Rapid Data Analytics Platform using Machine Learning

NASA#39;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.nbsp;In this proposal, we introduce a cutting-edge data analytics platform that uses a novel dimensional reduction algorithm to promote nbsp;optimal use of NASA#39;s growing collection of scientific data. Preliminary results show that when applied to a 2.2TB turbulent flow dataset our platform#39;s novel algorithms yield a 60-1000x reduction in download and storage costs and a 10-1000x reduction in computational costs, nbsp;depending on the desired accuracy in the solution.nbsp; nbsp;