MapReduce is a programming model for distributed data processing and execution environment that runs on large clusters of commodity machines. A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. The framework takes care of scheduling tasks, monitoring them and re-executes the failed tasks. MapReduce originated from Google research papers in 2004 and since then become the heart of Hadoop.
Extracting the numbers from the images is a complex task, but Google research papers it has developed a deep neural network system that can be trained to identify numbers of up to five digits long.
Discontinuous Seam Carving for Video: Google Research Paper
In 2009, the web giant started replacing GFS and MapReduce , and Mike Olson will tell you that these technologies are where the world is going. “If you want to know what the large-scale, high-performance data processing infrastructure of the future looks like, my advice would be to read the Google research papers that are coming out right now,” Olson said during a alongside Wired.
Like the rest of Google's much admired back-end infrastructure, Chubby is decidedly closed source, so men and built their own. Although they didn't have source code, they did have one of those secret-spilling Google research papers. And they had Go, Google's open source programming language for building systems like Google's.