Sentence examples for data mining and analyzing from inspiring English sources

From the perspectives of statistical computation and data mining, Ye et al. [94] presented an architecture of the services platform which integrates R to provide better data analysis services, called cloud-based big data mining and analyzing services platform (CBDMASP).

To determine whether ICAM-1 expression was associated with colon cancer in humans, we first performed data mining and analyzed ICAM-1 transcript by using the publicly available Oncomine database.

With the recent updates of databases, we have data mined and analyzed the possible orthologues of these 2 proteins in both lower and higher plants establishing its classification as a Class II histone deacetylase.

This course discusses data mining and machine learning algorithms for analyzing very large amounts of data.

The course will discuss data mining and machine learning algorithms for analyzing very large amounts of data.

Privacy and security perspective: The concern of data privacy has become extremely serious with using data mining and machine learning technologies to analyze personal information in order to produce relevant or accurate results.

Researchers are increasingly turning to cloud computing both as a solution to integrate data from genomics, systems biology and biomedical data mining and as an approach to analyze data to solve biomedical problems.

As a result, researchers are increasingly turning to cloud computing both as a solution to integrate data from genomics, systems biology and biomedical data mining and as an approach to mine and analyze data to solve biomedical problems.

It's one thing to replace existing compute resources with new machines capable of mining and analyzing data; many times just it calls for a simple rack substitution.

In recent years many data mining and visualization methods are developed that analyze and map spatial data at multiple scales for roadway inventory management purposes (Ashouri Rad and Rahmandad 2013).

When system servers receive real-time data from learner devices, intelligence and analytical tools such as learning analytics, data mining, and big data, are used to analyze and store the learning data, and then recommend learning patterns and resources to learners.