Sentence examples for data processing environment from inspiring English sources

The Durok system can be applied to small datasets that can be processed without a distributed data processing environment.

Workflow can be devised as a tool used purely in electronic data processing environment where data is to be transmitted across heterogeneous departments handled by personnel belonging to diverse engineering backgrounds.

Analytics Lab is an agile data processing environment using advanced education data analytics and visualizations to create HE dashboards to enhance decision-making and strategic planning.

Promoted as 'a business intelligence shared service for UK education' and a 'live data processing environment,' Analytics Lab emphasizes 'cutting edge data manipulation and analysis' and enables teams 'to acquire, combine and visualise national data sets helping the higher education (HE) sector exploit the benefits of business intelligence' (https://www.business-intelligence.ac.uk/).ac.uk/

However few of the open-source LIMS also provide the data processing environment.

To substantiate this approach we analyzed the current data processing environment to advance the unification of cancer documentation, e.g. for solid cancer entities.

This scheduling problem arises in batch data processing environments such as Map-Reduce systems, some of which handle tens of thousands of processing requests daily, over a shared set of files.

The coupling of "knowledge-base" and "data-base" demonstrates in a forceful way the manner in which an expert system can significantly enhance the throughput and quality of data processing environments supporting business management.

Oracle has integrated Cloudera's distribution including Apache Hadoop and Cloudera Manager software into its Big Data Appliance, a just launched engineered system designed to provide high performance and scalable data processing environments for Big Data.

Several platforms address some aspect of the problem like Big Data stacks [3, 45]; data processing environments (e.g., Hadoop, Spark, CaffeonSpark); data stores dealing with the CAP (consistency, atomicity and partition tolerance) theorem (e.g., NoSQL's); and distributed file systems (e.g., HDFS).

The standard GUI-based fMRI data processing environments, such as SPM, FSL [ 62] or AFNI [ 63], do not implement most of the modeling approaches described in this review.