We’ve joined forces with Smartlogic to reveal smarter decisions—together.

We used MongoDB early on, but we realized there are so many things that you can’t do with it, like built-in search, Semantics, and true RDF expression of our data models.”

Icon Customers Challenges


  • Data silos
  • Data integration
  • Inflexible and inaccessible software
  • Massive amounts of data (a simulation could produce an exabyte of data in one month)
  • Data governance
Icon Customers Solutions


Icon Customers Results


  • Realizing a performance increase of 100x
  • Securely discovering and sharing information
  • Reducing costs, development hours, and redundancies
  • Optimizing research resources

Why MarkLogic?

Early efforts with MongoDB showed that HyperThought held promise, but it required major effort to tether together critical missing pieces and derive benefit from the data. AFRL needed to add search, security, semantics, and performance at scale with a multi-model format; for these reasons, it selected MarkLogic to serve as the foundation for HyperThought.

Flexibility, Agility, Scalability

Adding new data sources, changing data models, and increasing data volumes are surmountable challenges with HyperThought.


Historical tests and results are now discoverable and usable, improving efficiency across development and research.


Scientists can now securely share data, which fosters collaboration with other researchers like Lawrence Livermore National Labs.

Related Resources

U.S. AFRL MarkLogic World Session 2018

Listen to U.S. AFRL explain how its MarkLogic-based Operational Data Hub is helping to create safer products.

Interview with Matthew Jacobsen of U.S. AFRL

Learn how U.S. AFRL breaks down silos to more efficiently create aircraft, weapons and more.

AFRL: Enabling Semantic Discovery in a Data Lake Context

Learn how U.​S.​ AFRL is using MarkLogic to make their data Findable, Accessible, Interoperable, and Reusable (FAIR).

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