I confess I haven't read the whole thing but that figure 4 looks more like a set of approaches, rather than a continuum as in the periodic table.

Their figure 1, which appears to describe fundamental design tradeoffs, makes me think of CAP theorem "maps", like this:

https://www.researchgate.net/figure/CAP-Theorem_fig4_2214620...

But the whole idea of figuring out first principles and "fundamental" design choices is quite appealing. They have a section saying that a big goal here would be automatic design of optimal data structures. They talk of machine learning techniques (they mention Bayesian optimization and reinforcement learning).

That seems a very interesting research direction. In the same vein there's this talk by Jeff Dean about how they use ML at Google to replace all sorts of heuristics in data structure design (bloom filters etc.) with machine learning to optimize performance, though from what I recall it doesn't automatically change the algorithm itself:

http://learningsys.org/nips17/assets/slides/dean-nips17.pdf

(discussed on HN previously https://news.ycombinator.com/item?id=15892956)

EDIT: I think they cite a paper by Dean and others which was part of that talk