NASA Upgrades Search Capabilities
NASA’s far-flung research centers have generated a solar system’s worth of data since the agency was launched in 1958. To help make it more searchable for rocket engineers and scientists, the space agency is enlisting new search methods based on machine learning frameworks.
NASA’s George C. Marshall Space Flight Center in Huntsville, Ala., will upgrade is search and analytics capabilities using AI and natural language processing (NLP) frameworks developed by cognitive search and analytics specialist Sinequa. The company is teaming with government integration contractor Science Applications International Corp. (NYSE: SAIC) to deploy a “global knowledge management” capability at the space agency largest center.
The partners said this week the NASA search project will utilize Sinequa’s NLP and machine learning technologies to uncover insights within NASA’s vast collections of data, “making historically unusable information now actionable in support of future mission planning.”
Sinequa’s insight engine is designed to help search and analyze NASA’s structured and unstructured content in support of missions and operations. Marshall is NASA’s primary spacecraft propulsion research center.
“We are helping NASA to access and utilize decades’ worth of information,” said Bob Genter, general manager of SAIC’s Civilian Markets Customer Group.
Among Marshall’s current missions is development of the Space Launch System designed to take astronauts beyond low-Earth orbit for the first time since the end of Project Apollo in the early 1970s.
Sinequa was recently ranked among the market leaders along with IBM (NYSE: IBM), Mindbreeze and Coveo in Gartner Inc.’s Magic Quadrant rankings of “insight engine” vendors.
Insight engines are defined as platforms that combine search with AI to deliver actionable insights derived from structured and unstructured data sourced from within and external to an enterprise.