Peregrine is a map reduce framework designed for running iterative jobs across partitions of data. Peregrine is designed to be FAST for executing map reduce jobs by supporting a number of optimizations and features not present in other map reduce frameworks.
MRQL (the Map-Reduce Query Language) is an SQL-like query language for map-reduce computations. It is implemented on top of Apache's Hadoop. MRQL is powerful enough to express most common data analysis tasks over many different kinds of raw data, including hierarchical data and nested collections, such as XML data. It is more powerful than other current languages, such as Hive and Pig Latin, since it can operate on more complex data and supports more powerful query constructs, thus eliminating the need for using explicit map-reduce code.
A list of Group papers for MapReduce Applications. Articles include: 'Nephele: Genotyping via Complete Composition Vectors and MapReduce' by Marc E Colosimo, Matthew W Peterson, Scott Mardis et al., 'Clustering Very Large Multi-dimensional Datasets with MapReduce' by Robson L F Cordeiro, Julio López, Christos Faloutsos and 'Yahoo! Research Small World Experiment' by Yahoo!, Facebook
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