-** Solution (the big idea)
-I see 4D data structure.
-
-[[file:data model.png]]
-
-Dimensions:
-+ List of all the objecs in the system (rows).
-+ List of all declared unique object fields (columns).
-+ List of all historical transactions/commits/versions (think of
- sheets of paper).
-+ List of all concurrently running branches/threads. Branches can
- appear and merge over time as needed.
-+ (Every cell is concrete field value within an object)
-
-Partitioning/clustering:
-+ Why not to partition/(load balance) as required across networked
- physical computers along arbitrary dimension(s) declared above ?
-
-Indexing (for fast searching):
-+ Why not to index along arbitrary dimensions (as required) ?
-
-Further optimizations:
-+ In current early stage, trying to focus on minimum possible set of
- features that would provide maximum possible set of power/benefit :)
-+ Once featres are locked. Anything can be optimised. Optimization for
- size (deduplication) can be solved using Git style content
- addressible storage mechanism.
++ Brain (appears to have more than 3D dimensional design. Food for
+ thought...)
+ + https://singularityhub.com/2017/06/21/is-there-a-multidimensional-mathematical-world-hidden-in-the-brains-computation/
+ + From there comes following idea: Maybe every problem can be
+ translated to geometry (use any shapes and as many dimensions as
+ you need). Solution(s) to such problems would then appear as
+ relatively simple search/comparison/lookup results. As a bonus,
+ such geometrical *data storage* AND *computation* can be
+ naturally made in *parallel* and *distributed*. That's what
+ neurons in the brain appear to be doing ! :) . Learning means
+ building/updating the model (the hard part). Question answering
+ is making (relatively simple) lookups (geometrical queries)
+ against the model.
+