We propose and evaluate a framework for creating and running approximation-enabled
MapReduce programs. Specifically, we propose approximation mechanisms that fit naturally …

Timely and cost-effective processing of large datasets has become a critical ingredient for
the success of many academic, government, and industrial organizations. The combination …

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 its …

The MapReduce distributed programming framework has become popular, despite
evidence that current implementations are inefficient, requiring far more hardware than a …

MapReduce is a programming model and an associated implementation for processing and
generating large datasets that is amenable to a broad variety of real-world tasks. Users …

Until now, design patterns for the MapReduce framework have been scattered among
various research papers, blogs, and books. This handy guide brings together a unique …

MapReduce has emerged as a viable competitor to database systems in big data analytics.
MapReduce programs are being written for a wide variety of application domains including …

MapReduce [5] is a well-known programming model, developed within Google, for
processing large amounts of raw data such as crawled documents or web request logs on a …

The work presented here is driven by two observations. First, heterogeneous architectures
that integrate a CPU and a GPU on the same chip are emerging, and hold much promise for …

Recently, there has been a huge growth in the amount of data processed by enterprises and
the scientific computing community. Two promising trends ensure that applications will be …