… tions of MapReduce, among which the most popular is the Hadoop … MapReduce and tested
it with the word-count use case. The key performance factor in implementing MapReduce is …

… of MapReduce using the PGAS programming model. The goal of this new approach is to put
together the potential of MapReduce … The proposed MapReduce implementation has been …

… implementations of Mapreduce is Hadoop framework and Google proprietary Mapreduce
system. … Main driving forces of modern big data development are powerful Mapreduce - based …

… of MapReduce using the PGAS programming model. The goal of this new approach is to put
together the potential of MapReduce … The proposed MapReduce implementation has been …

… This work presents an implementation of MapReduce usingthe PGAS programming model.
The goal of this new approachis to put together the potential of MapReduce for largedata …

… This paper proposes a solution to optimize the MapReduce program … In a fine-grain level,
however, Hadoop uses a hash function to … In this paper, we intend to improve the MapReduce …

… with the MapReduce approach, using Apache Hadoop TeraSort implementation. The …
MapReduce framework. In this paper, the PGAS approach is directly compared to the MapReduce …

… spill data in Hadoop/MapReduce MapReduce programming model is widely used in processing
massive data in parallel. As a widely used MapReduce platform, Hadoop was applied to …

… There were works that attempt to implement MapReduce functionality in PGAS lan… the
MapReduce framework. In this paper, the PGAS approach is directly compared to the MapReduce …

… engine, a Hadoop API compatibility layer allows unmodified Hadoop Map/Reduce jobs to …
– Enable application programmer to “pin” data in memory within Hadoop APIs – The reducer …