Research Summaries

Back Creating a Framework for USMC-Navy Big Data Management by ensuring II MEF framework supports the needs of Marine Expeditionary Force, Expeditionary Strike Group & Navy Aviation Mission Planners

Fiscal Year 2018
Division Research & Sponsored Programs
Department Naval Research Program
Investigator(s) Godin, Arkady A.
Sponsor NPS Naval Research Program (Navy)
Summary IREF addresses NPS-18-M173 (I MEF-III MEF) ($150k). This IREF also addressed Navy needs by supporting NPS-18-N217 ($100k) and NPS-18-N218 (ESG) ($120k) to establish an environment that can process the large amount of mission data collected in order to improve aviation mission planning, mission execution. This makes submitted IREF Joint
Ontologies should be extended for reasoning about complex events – in context, in real time. MEFs, in addition to real-time information, need to reason with prior knowledge (e.g., of prior battlespaces, of prior enemy intents). AFRL Rome Labs applied SBIR funding to fund research on a composable ontological representation engine (CORE) architecture. It is a warfighting integration architecture which provides composable interoperability across multiple domains. CORE architecture is based on research of typed ontologies grounded in computable types and composed via a grammar into arbitrarily complex events. CORE architecture implementation combines reasoning support of an ontological system with real-time data support of a streaming engine.
CORE generates computable expressions from the multi-sensory observation of the physical world. The language that defines expressions is captured in a knowledge representation that utilizes ontological typing. CORE is powered due to an ability to define compositional grammar of extensible inner layer of math types (e.g., Lists, Integers, Hierarchies, graphs, arrays) softly coupled with an ontological layer (that works with upper-level ontology and can ingest from any standard ontology) composed of a compact set of ontological types (e.g., Time, Space, Object, Process, Attribute) that bind, through the grammar, into a representation with ontological dimensions of structured sensor data, legacy structures, databases, and natural language. This requires an approach to capture all of the above in a shared representation necessary to manage accumulated knowledge
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