  Modern advances in space technology have enabled the capture and recording of
unprecedented volumes of data. In the field of solar physics this is most
readily apparent with the advent of the Solar Dynamics Observatory (SDO), which
returns in excess of 1 terabyte of data daily. While we now have sufficient
capability to capture, transmit and store this information, the solar physics
community now faces the new challenge of analysis and mining of high-volume and
potentially boundless data sets such as this: a task known to the computer
science community as stream mining. In this paper, we survey existing and
established stream mining methods in the context of solar physics, with a goal
of providing an introductory overview of stream mining algorithms employed by
the computer science fields. We consider key concepts surrounding stream mining
that are applicable to solar physics, outlining existing algorithms developed
to address this problem in other fields of study, and discuss their
applicability to massive solar data sets. We also discuss the considerations
and trade-offs that may need to be made when applying stream mining methods to
solar data. We find that while no one single solution is readily available,
many of the methods now employed in other data streaming applications could
successfully be modified to apply to solar data and prove invaluable for
successful analysis and mining of this new source.
