Special Issue: MapReduce and its Applications

Since its introduction in 2004 by Google, MapReduce has become the programming model of choice for processing large data sets. MapReduce borrows from functional programming, where a programmer can define both a Map task that maps a data set into another data set and a Reduce task that combines intermediate outputs into a final result. Although MapReduce was originally developed for use by web enterprises in large data-centers, this technique has gained much attention from the scientific community for its applicability in large parallel data analysis (including geographic, high energy physics, genomics, etc.). This special issue of Concurrency and Computation: Practice and Experience is a follow-up to the first Workshop on MapReduce and its Application held in 2010 with the ACM conference High-Performance Parallel and Distributed Computing. The MapReduce workshop attracted many international attendants, allowing deep discussion and the exchange of ideas and results related to ongoing research among attendants. The purpose of this special issue is to focus on recent advances in the developments of tools, applications and environments for MapReduce (or very similar) systems. We selected three contributions that investigate these issues, introduce new execution environments, apply performance evaluations and show the applicability to science and enterprise applications. The paper by Viken Valvåg et al. introduces Cogset, a new MapReduce engine that targets high performance [1]. The paper by Urbani et al. presents a scalable application of MapReduce to the problem of RDF data compression [2]. The paper by Schütt et al. presents MR-Search, a framework for massively parallel heuristic search [3].