  Unsupervised learning algorithms like self-organizing Kohonen maps are a
promising approach to gain an overview among massive datasets. With UltraPINK,
researchers can train, inspect, and explore self-organizing maps, whereby the
toolbox of interaction possibilities grows continually. Key feature of
UltraPINK is the consideration of versality in astronomical data. By keeping
the operations as abstract as possible and using design patterns meant for
abstract usage, we ensure that data is compatible with UltraPINK, regardless of
its type, formatting, or origin. Future work on the application will keep
extending the catalogue of exploration tools and the interfaces towards other
established applications to process astronomical data. Ultimatively, we aim
towards a solid infrastructure for data analysis in astronomy.
