There has been a tendency for astronomers to avoid unsupervised data exploration, due to the characterization of this approach as a non-scientific fishing expedition. But, a cognitive approach to massive data exploration has the potential to amplify hypothesis formulation and question generation for greater astronomical discovery. The incorporation of contextual data from other wavelengths and other surveys provides the basis for seeing interestingness in the multi-dimensional properties of sources that might otherwise appear uninteresting in a single survey database. Some suggested methods for cognitive exploration will be presented, including computer vision algorithms that are used in robotics to see patterns in the the world, but these can be used to see emergent patterns in the multi-dimensional parameter space of astronomical data.
Link to PDF (may not be available yet): I4-1.pdf