Datanova Scientific Proposal N201-009

Fusing different DHM (digital human modeling) outputs is not a DHM or simulation problem, it is a semantic data fusion problem. Our solution, PHOENIX, uses our data fusion expertise in healthcare, simulation, and industry to solve this problem. Using PHOENIX, we can fuse complex DHM data to create the larger human model in an agile and evolvable way. PHOENIX will not only incorporate DHM systems, but also seamlessly pull in context datasets (e.g. injury data), environmental data, and other data sources into a cohesive whole. PHOENIX uses an industry tested, open standards based, Fusion Ontology to efficiently align different DHM systems and other complex data. The Fusion Ontology allows powerful and expressive alignment of systems that are related to each other logically but are dissimilar enough to prevent straightforward API based fusion. Another core technology in PHOENIX are industry tested Scala Data Transformation DSLs (Domain Specific Languages). Datanova’s DSL technology allows for the easy configuration of complex data transformations from DHM system API to Fusion Ontology. They also allow fused data to be passed back to the source DHM systems as configuration. Thus, the PHOENIX system will also be used confiture and enrich the source DHM systems. Both the Fusion Ontology and Scala DSLs are being successfully used in healthcare, simulation, DoD intelligence, and industry. PHOENIX data fusion is Minimal and Agile: Creating a single DHM effort for the larger human body is prohibitively complex. The Fusion Ontologies solve this problem: (1) They only create the minimal needed to align two DHM systems (2) Multiple Fusion Ontologies can coexist, (3) Fusion Ontologies will leverage exiting work done in DHM, (4) Ontologies grow and connect over time, making sure no effort is wasted along the way. Powerful: PHOENIX DSL technology can handle all types of transformations needed, such as data format, units of measure, data structure, etc. The Fusion ontology can model any existing DHM models and equations into its relationships. This informs the Scala DSLs, which uses that information to correctly transform the data to fuse the output to allow interoperation.