Min(d)ing the small details: discovery of critical knowledge through precision manifold learning, and application to onboard decision support

Fast identification of critical information in a changing environment is difficult yet it is key to dynamical decision support, in general. Finding critical information in large and complex data volumes is a challenge real systems, and systems of systems, pose increasingly. Moreover, many of these real systems are desired to operate highly autonomously, using extracted critical information and discovered and distilled knowledge directly, for decisions. Spacecraft or rover navigation based on scientific findings from continuously collected data by onboard computation, is one example. This highlights the importance of the quality of information extraction. The knowledge discovery process must be intelligent enough to produce useful details; reliable; robust; and fast. This paper focuses on the first three of these quality aspects through precision manifold learning, in an onboard decision making scenario of a space mission.