  At the Canadian Astronomy Data Centre, we have combined our cloud computing
system, CANFAR, with the world's most advanced machine learning software,
Skytree, to create the world's first cloud computing system for data mining in
astronomy. CANFAR provides a generic environment for the storage and processing
of large datasets, removing the requirement to set up and maintain a computing
system when implementing an extensive undertaking such as a survey pipeline.
500 processor cores and several hundred terabytes of persistent storage are
currently available to users. The storage is implemented via the International
Virtual Observatory Alliance's VOSpace protocol, and is accessible both
interactively, and to all processing jobs. The user interacts with CANFAR by
utilizing virtual machines, which appear to them as equivalent to a desktop.
Each machine is replicated as desired to perform large-scale parallel
processing. Such an arrangement enables the user to immediately install and run
the same astronomy code that they already utilize, in the same way as on a
desktop. In addition, unlike many cloud systems, batch job scheduling is
handled for the user on multiple virtual machines by the Condor job queueing
system. Skytree is installed and run just as any other software on the system,
and thus acts as a library of command line data mining functions that can be
integrated into one's wider analysis. Thus we have created a generic
environment for large-scale analysis by data mining, in the same way that
CANFAR itself has done for storage and processing. Because Skytree scales to
large data in linear runtime, this allows the full sophistication of the huge
fields of data mining and machine learning to be applied to the hundreds of
millions of objects that make up current large datasets. We demonstrate the
utility of the CANFAR+Skytree system by showing science results obtained.
[Abridged]
