> On Jun 24, 2015, at 9:39 AM, Jim Bromer <[email protected]> wrote:
> 
> I think Data types with no meaningful order can be difficult but it is
> not impossible to create things like keyed (I can't remember what the
> data structure is called) indexes and even more elaborate indexes as
> needed. But this can become a more serious issue when you have to have
> a lot of specialized indexes.


You can do it all within a single (exotic) quasi-spatial indexing structure. It 
is how it is done in real systems.


> I can intuitively see that data types where intersection
> and equality are not equivalent could be a problem but I am not sure
> what you mean.


Dynamic search algorithms tend to have very poor selectivity traditionally. 
O(n) worst-case search is no way to build a scalable computing system.


> Since you have derived more than one example of
> bad-computer science thinking from relational database concepts I am
> guessing that this has something to do with database processing. So
> searching on a constraint can become time consuming? But that can be
> parallelized by minor redesigns.


It has nothing to do with databases, though these issues are manifest in 
large-scale databases. Parallelization doesn’t work the way you think it does.

Let’s keep make it even simpler: please describe an indexing structure for 
finding cube intersections that is general, parallelizable, and has constant 
space complexity. A “minor redesign” won’t solve this problem. When it was 
finally solved in 2007, over a quarter century had passed since anyone had 
previously made progress on it, and I am willing to bet that you know nothing 
about what the actual solution looks like.


> Many computer scientists have thought about expressing topological
> relationships between data objects. So it is not in itself a new idea
> that I have never heard of or thought about before.



The mathematical concept has existed for half a century. A useful computer 
science reduction of the mathematics is maybe five years old. I doubt what you 
think you know about this idea is relevant.





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AGI
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