Recent trends in physics-based and human-derived information fusion (PHIF) have amplified the capabilities
of analysts; however with the big data opportunities there is a need for open architecture designs, methods of distributed
team collaboration, and visualizations. In this paper, we explore recent trends in the information fusion to support user
interaction and machine analytics. Challenging scenarios requiring PHIF include combing physics-based video data
with human-derived text data for enhanced simultaneous tracking and identification. A driving effort would be to
provide analysts with applications, tools, and interfaces that afford effective and affordable solutions for timely decision
making. Fusion at scale should be developed to allow analysts to access data, call analytics routines, enter solutions,
update models, and store results for distributed decision making.