Sentence examples for big data analysis methods from inspiring English sources

In addition to the aforementioned improvements in study design and larger cohorts with limited confounding, advancements in big data analysis methods should also help to facilitate within- and between-study analyses.

Several important concepts in the design of the big data analysis method will be given in the following sections.

Here, we review several recent applications of the big and deep data analysis methods to visualize, compress, and translate this multidimensional structural and functional data into physically and chemically relevant information.

For the input (see also in "Big data input") and output (see also "Output the result of big data analysis") of big data, several methods and solutions proposed before the big data age (see also "Data input") can also be employed for big data analytics in most cases.

We explored the performances of methods dedicated to big data analysis for detecting independent associations between exposures and a health outcome.

According to our observation, most data analysis methods have limitations for big data, that can be described as follows: Unscalability and centralization Most data analysis methods are not for large-scale and complex dataset.

Redesigning and changing the way the data analysis methods are designed are two critical trends for big data analysis.

This study overcomes the limitations of previous methods of technology forecasting that have depended on expert opinion or peer review by performing a patent big data analysis to ascertain the domain of the BIM industry.

DataSift had used it to perform "big data" analysis for clients in all sorts of fields.

The telemetry is used for "big data" analysis based on more than 400m Windows 10 systems.

Because the traditional data analysis methods are not designed for large-scale and complex data, they are almost impossible to be capable of analyzing the big data.