Intuitive Information Fusion and Visualization

Traditional Information Fusions (IF) systems need to be expanded to encompass a decision support system (DSS) role that enables analysts and commander to manage the volume and heterogeneity of sensor data, including the textual"soft"data descriptive of the social cultural landscape characteristics of contemporary missions. To achieve these improvements, SoarTech, supported by NCSU, will develop Sensemaking Patterns for Analysis and Decision-making (SPADE) a hybrid human computer interaction IF/DSS systems that draws its requirements from abductive hypothesis generation, a feature of sophisticated human decision models including sense-making and recognition primed decision-making. SPADE will apply hierarchical task network and hypothesis graphs to support temporal, spatial, semantic hypothesis definitions. SPADE uses user populated hypothesis patterns to auto-generate and adapt queries for existing, vetted data processing algorithms such as sentiment analysis and text indexing. This will enable analysts and decision makers to easily encode analytic and mission-planning hypothesis, including decision and time constraints, and automatically gain data management and integration, emergent concept detection, and hypothesis testing across traditional INTs and social data. SPADE will better support the coupling of data and decision needs, better mitigate analysis biases, and better support analysis and decision-making under uncertainty and time constraints.