  Wikipedia, an open collaborative website, can be edited by anyone, even
anonymously, thus becoming victim to ill-intentioned changes. Therefore,
ranking Wikipedia authors by calculating impact measures based on the edit
history can help to identify reputational users or harmful activity such as
vandalism \cite{Adler:2008:MAC:1822258.1822279}. However, processing millions
of edits on one system can take a long time. The author implements an open
source framework to calculate such rankings in a distributed way (MapReduce)
and evaluates its performance on various sized datasets. A reimplementation of
the contribution measures by \citeauthor{Adler:2008:MAC:1822258.1822279}
demonstrates its extensibility and usability, as well as problems of handling
huge datasets and their possible resolutions. The results put different
performance optimizations into perspective and show that horizontal scaling can
decrease the total processing time.
