Abstract—MapReduce is emerging as a generic parallel programming paradigm for large clusters of machines. This trend combined with the growing need to run machine learning (ML) a...
Amol Ghoting, Rajasekar Krishnamurthy, Edwin P. D....
MapReduce is a computing paradigm that has gained a lot of popularity as it allows non-expert users to easily run complex analytical tasks at very large-scale. At such scale, task...
The MapReduce distributed programming framework has become popular, despite evidence that current implementations are inefficient, requiring far more hardware than a traditional r...
Eaman Jahani, Michael J. Cafarella, Christopher R&...
-- The MapReduce programming model, introduced by Google, has become popular over the past few years as a mechanism for processing large amounts of data, using sharednothing parall...
Sriram Krishnan, Chaitanya K. Baru, Christopher J....
Large-scale data analysis has become increasingly important for many enterprises. Recently, a new distributed computing paradigm, called MapReduce, and its open source implementat...
MapReduce is a computing paradigm that has gained a lot of attention in recent years from industry and research. Unlike parallel DBMSs, MapReduce allows non-expert users to run co...
Google's MapReduce programming model serves for processing large data sets in a massively parallel manner. We deliver the first rigorous description of the model including it...
Background: MapReduce is a parallel framework that has been used effectively to design largescale parallel applications for large computing clusters. In this paper, we evaluate th...
MapReduce programming model has simplified the implementation of many data parallel applications. The simplicity of the programming model and the quality of services provided by m...
MapReduce is a popular framework for data-intensive distributed computing of batch jobs. To simplify fault tolerance, many implementations of MapReduce materialize the entire outp...
Tyson Condie, Neil Conway, Peter Alvaro, Joseph M....