Sentence examples for big data reduction methods from inspiring English sources

These adoptions open new research avenues for interdisciplinary research and develop novel big data reduction methods.

A thorough literature review and classification of big data reduction methods are presented.

The taxonomical discussion on big data reduction methods is presented in Sect.

The similarity-based big data reduction methods are good choice for network extraction and reduction.

The core technological support for big data reduction methods is based on multilayer architecture (see Fig. 1).

The main contributions of this article are: A thorough literature review and classification of big data reduction methods are presented.

Although the cluster-level deduplication is effective for big data reduction, new deduplication methods are required to improve energy efficiency and resource awareness in large-scale data centers.

Moreover, the DM and ML methods are equally useful for big data reduction when coupled with artificial intelligence-based optimization methods.

This article presents a thorough literature review of methods for big data reduction.

However, both of the studies lack in presenting discussion about specific systems and methods for big data reduction.

Sampling and compression are two representative data reduction methods for big data analytics because reducing the size of data makes the data analytics computationally less expensive, thus faster, especially for the data coming to the system rapidly.