Finding the Critical Sampling of Big Datasets
José Tomás da Silva, Bernardete Ribeiro, Andrew H. Sung · 2017
Big Data allied to the Internet of Things nowadays provides a powerful resource that various organizations are increasingly exploiting for applications ranging from decision support, predictive and prescriptive analytics, to knowledge extraction and intelligence discovery. In analytics and data mining processes, it is usually desirable to have as much data as possible, though it is often more important that the data is of high quality thereby two of the most important problems are raised when handling large datasets: sampling and feature selection. This paper addresses the sampling problem and presents a heuristic method to find the "critical sampling" of big datasets.