Sentence examples for data analysis and storage from inspiring English sources

While they're not yet being used to provide services for customers, they are being tested for uses such as machine learning, big data analysis, and storage.

Capabilities include microarray data analysis and storage, sequence and pathway analysis, and extend all the way to MAGNet tools for Systems and Structural biology.

The needs for in-flight data analysis and storage in the Antarctic and Arctic are highly unusual, and we have developed a novel system to meet those needs.

Since the start of the Joint European Torus JETT), an IBM mainframe has been the main platform for data analysis and storage (J. Comput. Phys. 73 (1987) 85).

Bioinformatics, data analysis and storage platforms should be developed to facilitate rapid processing of large datasets, especially when mammalian tools such as bioinformatics analysis software are not available in fishes.

Review, development, modification, and approval of requirements for cloud computing solutions for data analysis and storage by the Armed Forces and the Defense Agencies, including requirements for cross-domain, enterprise-wide discovery and correlation of data stored in cloud and non-cloud computing databases, relational and non-relational databases, and hybrid databases.

The survey reveals that big data reduction is performed at many levels during the data processing lifecycle that include data capturing, data preprocessing, data indexing and storage, data analysis, and visualization.

Big Data creates new requirements based on complexities in data capture, data storage, data analysis and data visualization.

Big Data life cycle is composed by five phases: data sources, data integration, data storage, data analysis and data visualization Fig. 6 Big Data analytics for smart grid.

Through the abovementioned research, a third-party forecasting and early warning system for pipeline were established, including data acquisition, data storage, data analysis and modeling, data risk visualization, and trend analysis.

In order to get going with big data and turn it into insights and business value, it's likely you'll need to make investments in the following key infrastructure elements: data collection, data storage, data analysis, and data visualization/output. Let's look at each area in turn.