Why is it that they aren't published? Competitive advantage?
I've done work in other algorithm areas where the situation is similar. It often costs millions of dollars to develop major computer science advances but the best way to recover that investment is to leverage it without publishing it. Reverse engineering a thoroughly obfuscated technology takes a lot longer than when someone publishes the blueprints.
I will add that a lot of these algorithms are not at all obvious until you understand how they work. While knowing something is possible helps, it still requires a fair amount of theoretical computer science cleverness.
Space decomposition structures take a metric space and decompose it using an invariant space partitioning function. The partition points are fixed regardless of data distribution, the only decision process is when to apply the partitioning function.
The two well-known examples in literature are quad-trees and distributed hash tables. These are both very simple, narrow examples of space decomposition structures in a universe of possible metric spaces and possible partitioning functions. Many parallel data problems can't be made to fit into the simple space decomposition structures computer scientists are familiar with.
When massively parallel machinery is the foundational assumption, every computational structure must be represented in terms of space decomposition primitives. Right now, most computer scientists treat space decomposition structures as dumb data buckets and are largely oblivious to their expressiveness. Most published parallel algorithm computer science is based on computational structures that will only sort of parallelize on sufficiently small systems.
This is very interesting. Is there an example of the expressiveness of spatial decomposition you could share?