Google

(redirected from MapReduce)
Also found in: Dictionary, Thesaurus, Encyclopedia, Wikipedia.

Google

A publicly-traded company primarily offering a web portal. Originally a search engine in the 1990s and early 2000s, Google expanded to online messaging, e-mail services, web video, mobile phones and so forth. Google monetizes its operations primarily through advertisements. It was founded in 1998. See also: Yahoo.
References in periodicals archive ?
The architectural framework of MapReduce is shown in Figure 1.
One of the key Hadoop components is the MapReduce on which the other, higher-level Hadoop-related components rely, e.
Schatz, "CloudBurst: highly sensitive read mapping with MapReduce," Bioinformatics, vol.
In order to process huge data efficiently, the MapReduce framework, originally proposed by Google Research (Dean and Ghemawat, 2004) with its underlying distributed file system (Ghemawat et al.
BI Research analyst Colin White said, "This boosts Teradata's story for analytics on large and complex data sets, thanks to Aster's focus on in-database MapReduce analytic processing of multi-structured data, making this capability more accessible for organizations through standard SQL and pre-packaged MapReduce-based modules.
Key words: MapReduce, grid computing, distributed computing, hadoop, evolutionary algorithms
In addition, Tableau also announced that it has beta-released a direct connector for both Amazon (NASDAQ: AMZN) Elastic MapReduce from Amazon Web Services, as well as Spark SQL and that it has qualified for Databricks "Certified on Spark" program.
The interests in MapReduce program model resurgences in 2006, social network and multimedia develop at fantastic speed at that time.
Apache Hadoop [3]: software for data-intensive distributed applications, based in the MapReduce programming model and a distributed file system called Hadoop Distributed Filesystem (HDFS).
While the previous MinuteSort record was achieved with custom hardware, MapR set the record using commercially available Google Compute Engine, Hadoop MapReduce and the MapR Distribution.
After an overview of cloud computing, they consider such topics as resource scheduling, multi-dimensional data analysis in a cloud datacenter, data intensive applications with MapReduce, multi-dimensional data analysis optimization, and real-time scheduling with MapReduce.