This project has a great academic impact, across and within different disciplines, including significant advances in methods, theory and applications (1) It established an optimised framework based on mathematical theories for big data mining in certain and imprecise contexts. (2) It developed automated dimensionality reduction techniques, without requiring extra information, which improves the state-of-the-art methods. It performs feature selection by automatically discovering and extracting the most useful features with less information loss. (3) It dealt with the big data veracity aspect, which is beyond state-of-the-art. It handles data veracity in the most convenient way and provides the basis for new tools to cover the imprecise space.

By applying RoSTBiDFramework to large pools of data gathered from any discipline or sector, only valuable and fine-grained data is generated to discern patterns and improve decision-making. The RoSTBiDFramework output (the pre-processed data) will become the main source of competition and growth for a variety of industries and businesses, allowing them to enhance their productivity and to create important value for the world economy.