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.”

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
Solutions

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.

Efficiency

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

Collaboration

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

Related Resources

Video
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.

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