Google introduced the MapReduce algorithm to perform massively parallel processing of very large data sets using clusters of commodity hardware. MapReduce is a core Google technology and key to ...

It is an open implementation of the MapReduce algorithm and includes HDFS (Hadoop Distributed File System) for high throughput access to distributed data. What has been less visible for some time is ...

Google’s MapReduce algorithm turns a bunch of cell phones into a self-contained cloud computing environment.

The MapReduce design pattern to distribute data processing was introduced by Google in 2004, and came first with a C++ implementation. A new Ruby implementation is now available under the name of ...

We just follow the MapReduce pattern and Hadoop does the rest. MapReduce with Hadoop Hadoop is mostly a Java framework, but the magically awesome Streaming utility allows us to use programs written in ...

An Efficient Implementation of Apriori Algorithm Based on Hadoop-Mapreduce Model Finding frequent itemsets is one of the most important fields of data mining.

Hadoop is an open-source software framework that evolved from Google's MapReduce algorithm. Many Internet giants rely on Hadoop to quickly identify and serve customized data to consumers. In 2010 ...

Some algorithms translate poorly to Map-Reduce—the partitioning of data and computation to individual nodes makes some computations (graph processing for instance) difficult. And, the implementation ...

Cascading is a new processing API for data processing on Hadoop clusters, and supports building complex processing workflows using an expressive, declarative API.

"We knew that we were going to have to take Hadoop beyond MapReduce," Murthy says. "The programming model—the MapReduce algorithm—was limited. It can't support the very wide variety of use-cases we're ...