Emergent concept detection
Assuming the future availability of some sort of not yet existing (state 2017) multi criterion file system where every file and folder can have arbitrary many "super folders" (here: criteria). A system in which the data can be managed in such a way that it can be categorized by multiple criteria at the same time.
In such a system it will regularly happen that data starts to cluster together in similar patterns. A Cluster is formed when various multi-sorted pieces of data are multi-sorted in exactly or at least roughly the same combination of super-criteria. While not recognized by humans this easily can be detected in an automated way without much of intelligence and pointed out back to humans again which will lead to unexpected surprises (discoveries of new concepts).
This is so obvious that there are certain niche systems that do such a thing (and much more ambitious things) already (2017). Examples:
- google, but this is non-local and centralized only.
- some big databases ?
- ...
The core data management in personal computing can't do even this most trivial form of "emergent concept detection" because it's still locked in the hirachical/tree-topology file systems of today (2017..2018) where every file has to have exactly one super-folder.
It seems it really becomes increasingly urgent to resolve that giant roadblock.
Other ways new concepts are discovered:
- The simple "matrix method" is often surprisingly effective. Spotting the blanks.
But current data management systems (not only file systems but wikis too) do not let one specify two criterions as orthogonal. - Constructive recreation from special cases after from an formerly abstracted generalization often leads to more and quite unexpected special cases that the ones that motivated one to do the abstract generalization in the first place. (wiki-TODO: add the already existing illustrative graphic here)
Related
Depending on preexisting "views" sometimes "concepts" are detected despite not being present or differently present:
