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 patent on MapReduce could potentially pose a problem for those using third-party open source implementations. Patent #7,650,331, which was granted to Google on Tuesday, defines a system ...

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 ...

After a brief review of how map-reduce works, we shall look at the trade-off that needs to be made when designing map-reduce algorithms for problems that are not embarrassingly parallel. In particular ...

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.

Distributed programming models such as MapReduce enable this type of capability, but the technology was not originally designed with enterprise requirements in mind. Now that MapReduce has been ...

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 ...

"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 ...