Chenyi Hu, professor and chairman of UCA’s Computer Science department, received a $150,000 grant from the National Science Foundation for a project focused on knowledge processing with interval methods.
Knowledge processing with interval methods presents qualitative properties as ranges of data attributes rather than specific points. By grouping attribute values into meaningful intervals, insignificant quantitative differences can be omitted in favor of qualitatively processed datasets. More importantly, interval-valued attributes contain more information than points, and represent variability and uncertainty. In practice, interval-valued computational results can be more meaningful and useful than point value. Therefore, expanding current knowledge to intervals allows for additional, if not more powerful, tools for knowledge processing.
