  Scientific endeavors such as large astronomical surveys generate databases on
the terabyte scale. These, usually multidimensional databases must be
visualized and mined in order to find interesting objects or to extract
meaningful and qualitatively new relationships. Many statistical algorithms
required for these tasks run reasonably fast when operating on small sets of
in-memory data, but take noticeable performance hits when operating on large
databases that do not fit into memory. We utilize new software technologies to
develop and evaluate fast multidimensional indexing schemes that inherently
follow the underlying, highly non-uniform distribution of the data: they are
layered uniform grid indices, hierarchical binary space partitioning, and
sampled flat Voronoi tessellation of the data. Our working database is the
5-dimensional magnitude space of the Sloan Digital Sky Survey with more than
270 million data points, where we show that these techniques can dramatically
speed up data mining operations such as finding similar objects by example,
classifying objects or comparing extensive simulation sets with observations.
We are also developing tools to interact with the multidimensional database and
visualize the data at multiple resolutions in an adaptive manner.
