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The tens of millions of radio sources to be detected with next-generationsurveys pose new challenges, quite apart from the obvious ones of processingspeed and data volumes. For example, existing algorithms are inadequate forsource extraction or cross-matching radio and optical/IR sources, and a newgeneration of algorithms are needed using machine learning and othertechniques. The large numbers of sources enable new ways of testingastrophysical models, using a variety of "large-n astronomy" techniques such asstatistical redshifts. Furthermore, while unexpected discoveries account forsome of the most significant discoveries in astronomy, it will be difficult todiscover the unexpected in large volumes of data, unless specific software isdeveloped to mine the data for the unexpected.

Astroinformatics Challenges from Next-generation Radio Continuum Surveys