  The tens of millions of radio sources to be detected with next-generation
surveys pose new challenges, quite apart from the obvious ones of processing
speed and data volumes. For example, existing algorithms are inadequate for
source extraction or cross-matching radio and optical/IR sources, and a new
generation of algorithms are needed using machine learning and other
techniques. The large numbers of sources enable new ways of testing
astrophysical models, using a variety of "large-n astronomy" techniques such as
statistical redshifts. Furthermore, while unexpected discoveries account for
some of the most significant discoveries in astronomy, it will be difficult to
discover the unexpected in large volumes of data, unless specific software is
developed to mine the data for the unexpected.
