Existing Big Data solutions are mainly focused on the discovery and analysis of data. The solutions are scalable and highly available but tedious when swapping in and swapping out occurs in disarray and thrashing takes place. The resolution for thrashing through machine learning algorithms and support nomenclature is through simple techniques. Organizations that have been collecting large customer data are increasingly seeing the need to use the data for swapping in and out and thrashing occurs in both transaction processing and online analytical processing. Therefore, there is a growing need for support on thrashing using machine learning algorithms and solutions for use in «Big Data.»