Probability Analyses using Narrative-based Operational Frames of Reference for Accelerated Monitoring and Alerting (PANORAMA)

Intelligence, surveillance, and reconnaissance (ISR) sensors and processing technologies provide access to ever-increasing amounts of diverse intelligence data of variable quality. Wide area motion imagery (WAMI) sensor data, in particular, threatens to overload analyst resources and abilities to make sense of data in or near real time. To address these needs and develop improved human-in-the-loop workflows, we propose to design and demonstrate a system for Probability Analyses using Narrative-based Operational Frames of Reference for Accelerated Monitoring and Alerting (PANORAMA). First, we extend prior cognitive analyses conducted with intelligence community practitioners to define requirements for supporting and improving analyst workflows. Next, we will design a system workflow based on narrative understanding to provide contextual data framing. Then, we will design probabilistic models to automate the analysis of sensor data and enable probabilistic predictions of potential outcomes. Finally, we will design a workspace to support analyst insight to and direction of automated analyses, represent and visualize data in the context of narrative-based frames of reference, and alert analysts to emergent trends. Combined, PANORAMA-based tools will facilitate intuitive visualization, interaction, and teaming with new automated, probabilistic models as part of effective and efficient analyst workflows.