NASA Unexpected Pattern Discovering by using Graph Databases
NASA famously maintains a “lessons learned” database containing valuable information from its past programs and projects. Lessons Learned spans a half-century of collective NASA engineering knowledge, all the way back to the legendary Apollo moon missions.

Not too long ago, an engineer on the Orion spacecraft had a major problem, David Meza, Chief Knowledge Architect at NASA’s Johnson Space Center, recently explained to Business Insider. Orion’s uprighting system, which flips the returning capsule right-side-up after it splashes down in the ocean, wasn’t working correctly. In tests and simulations, the capsule stayed on its side, even after hitting the water.

There was every possibility that somewhere in Lessons Learned was the key to fixing Orion. But that massive database is no good if you can’t find anything. NASA’s original web search for Lessons Learned couldn’t turn anything up; even NASA’s History Office only turned up 3 relevant files in eight days of looking.

That’s where Meza and his team came in. Meza had been experimenting with a new, smarter, data-driven approach to sifting the Lessons Learned database. Within three hours, Meza’s better Lessons Learned had come up with 30 relevant documents. One the first 10 documents had cure for what ailed Orion. The mission was back on track.

Meza cites the Orion case as a big reason why his team has embraced a newer kind of database, growing in popularity among developers, called a “graph database,” a term first popularized by Neo Technology. Neo makes the Neo4j software, used by customers including Marriott, Monsanto, Walmart, and Meza’s team at NASA. 

See: Neo4j Graph Database

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