I am the originator, or at least one of the originators, for a method called categorical deduction. This may have a promising role in relation to big data as described in the article, through the use of coherent data-points and deductions performed upon objective tables of correspondence or coherent sets such as truth-tables (and similar operati).



The general method of categorical deduction is explained in my book, The Dimensional Philosopher's Toolkit (2013, 2014). But, basically, it follows the method of A-B : C-D and A-D : C-B using polar opposites or typologically and cyclically related category sets. Because all words are potentially opposites, according to my theory, some level of coherent detail is possible about any given data-point that can be explained as a category-set.



I advise that those wishing for an integration of virtual reality with social media, wishing to investigate the future of public interface or intelligent A.I. look into my book. Although it is unassuming in its message, the technique of categorical deduction simplifies data by a factor of ^2, what I call exponential knowledge. So, therefore, it is strange to call it a development upon previous methods. It is more like a revolution. This is like quantum computing without quantum technology. Big stuff. And apparently, it applies to big data as well.