> On Jun 22, 2015, at 6:57 PM, Samantha Atkins <[email protected]> wrote:
> 
> I seldom ask this as I usually work at the level of abstractions.  But could 
> you please give some examples of topological relationships that are difficult 
> to express computationally?  I am not sure I follow exactly what you have in 
> mind.  


There are two common cases handled poorly in computer science that are 
manifestations of topological relationships:

- data types with no meaningful order (e.g. graph edges)
- data types where intersection and equality are not equivalent (e.g. 
constraints or geospatial)

In other words, “data that is not sortable” and “data that is not partitionable 
(also not sortable)”. Computing on representations of these data models are 
infamously difficult to parallelize and scale because there is no 
relationship-preserving mapping to integers, and traditionally we rely on the 
sortability and partitionability of integers to scale algorithms. You just 
cannot usefully represent what are essentially spatial relationships this way.

However, both of these types of data models can be trivially embedded in a 
surface, and while a lot less obvious, you can parallelize computations on the 
data by manipulating the surface rather than the data directly. For example, 
the concept of a join in such a representation is closely related to 
topological homomorphism.

Another way of thinking about it:

What would data structures and algorithms literature look like if the most 
primitive data type was a hyper-rectangle instead of an integer? A 
hyper-rectangle is nothing more than a data type that cannot be represented by 
less than two integers of arbitrary dimensionality. Some important data types 
and relationships are of this nature. In this model, an integer is just a 
degenerate hyper-rectangle (i.e. volume is zero). 

Cheers,
Andrew



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