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Data analytics gets faced with huge and tremendously increasing amounts of data for which MapReduce provides a very convenient and effective distributed programming model. Various algorithms already support massive data analysis on computer clusters but, in particular, distance-based similarity self-joins lack efficient solutions for large vector data sets though they are fundamental in many data mining tasks including clustering, near-duplicate detection or outlier analysis. The authors' novel distance-based self-join algorithm for MapReduce, MR-DSJ, is based on grid partitioning and delivers correct, complete, and inherently duplicate-free results in a single iteration. Additionally they propose several filter techniques which reduce the runtime and communication of the MR-DSJ algorithm. Analytical and experimental evaluations demonstrate the superiority over other join algorithms for MapReduce.
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