Publication
Distributed Pattern Recognition in RapidMiner
Alexander Arimond; Christian Kofler; Faisal Shafait
In: RapidMiner Community Meeting and Conference. RapidMiner Community Meeting and Conference (RCOMM-10), September 13-16, Dortmund, Germany, Online, 9/2010.
Abstract
RapidMiner already provides easy to use interfaces for developing and evaluating Pattern Recognition
and Machine Learning applications. However, it has only
limited support for parallelization and it lacks functionality
to spread long-running computations over multiple machines.
A solution to this is distributed computing with paradigms
like MapReduce. In this paper, we present a system called
DisPaRe, which integrates distributed computing frameworks
into RapidMiner. A special focus is put on utilizing MapReduce
as a programming model. The frameworks GridGain and
Oracle Coherence are reviewed and evaluated with respect
to their suitability to fit into the context of RapidMiner. The
system provides effective means for transparently utilizing these
frameworks and enabling RapidMiner processes to parallelize
their computations within a distributed environment.