Natural-language Ontologies for battlespace Visualizations and Analytics (NOVA)

Modern warfare demands rapid, data-driven decision-making to maintain a tactical advantage over near-peer and peer threats. However, current command and control (C2) systems rely on outdated technology that does not fully leverage rich data available from sources throughout the theater of operations. This lack of advanced data processing hampers warfighters' ability to assess threats, predict enemy movements, and respond effectively in dynamic combat environments. The Maritime Tactical Command and Control (MTC2) system is being developed as the Navy’s next-generation C2 platform. MTC2 aims to replace legacy planning tools, introduce advanced decision aids, and enhance situational awareness through a Common Operational Picture (COP). A key requirement for MTC2 is the ability to process and represent data in a way that quickly creates understanding, moving beyond rigid schemas to a flexible, human- and machine-readable format. For effective battlefield decision support, MTC2 must integrate artificial intelligence (AI), machine learning (ML), and graph-based analytics to enable near-real-time platform and weapons suitability determination. Additionally, the system must support low-code/no-code data analytics, allowing warfighters to customize applications without extensive programming knowledge. By evolving data representation and enhancing speed of analysis, MTC2 will support the kill chain process, ensuring faster threat predictive capabilities and greater operational effectiveness in theater. To meet the above challenges and provide the desired capabilities, Machina Cognita Technologies (MCT) proposes Natural-language Ontologies for battlespace Visualizations and Analytics (NOVA) System, shown above in Figure 1. NOVA is an end-to-end data management solution for operational-level battlespace command and control (C2) decision support. Domain-specific Ontologies will provide universal schema-less human and machine-understandable data representation across all C2 data sources. A large language model (LLM)-enabled data translation pipeline will dynamically express multimodal data and rapidly retrieve C2 information. Finally, the NOVA data management system powers follow-on COP microservices, no/low code analytics, and a dedicated client application to reduce kill chain analysis, reduce warfighter workload, and increase situational awareness. Overall, the NOVA system will streamline multi-modal data representation across battlespace COP sources, enhance warfighter situational awareness, and accelerate kill chain analytics.