ORCA Data Sheet
BigBear.ai designed, developed, and operates a modernized maritime characterization and performance system that is replacing the Naval Intelligence Database (NID), aligned with all Defense Intelligence Machine Assisted Rapid-repository Services (MARS). ORCA is highly scalable (specific data to big data), cloud-native, and modular (open development standards). It is advanced computing enabled (Artificial Intelligence and Machine Learning) and leverages open-source technology platforms (i.e. KNIME and Neo4J) making the platform highly extensible.
The system provides both machine interfaces and graphical user interfaces for all business processes, including data input, exploration and discovery, workflow management, and data export. To succeed, the ORCA team had to engage closely with analysts to understand their processes and frustrations with legacy systems and perform thorough technical assessments of those systems to ensure new designs were able to mitigate identified issues.
Our team followed Agile methods to ensure users were involved in iteratively improving designs and guiding ongoing development, resulting in high user satisfaction and usable deliveries early in the contract’s period of performance. ORCA is the next-generation solution for the Office of Naval Intelligence (ONI) in handling the characterization of Naval threats. This encompasses conversion of the ONI database into a new graph database to alleviate the pain points that ONI experiences with their current Intelligence data solution.
Because the graph database is schemaless, the BigBear.ai solution allows the ORCA users to quickly add new entities and attributes to the database as soon as they are needed. The graph database solution performs and scales much better than relational databases when working with the highly connected and hierarchical data at ONI.
Finally, this graph database solution will also be the used as the central repository for Machine Learning as graph databases can highlight insights you would not see in a traditional relational database.
