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Great, thanks!

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In Prolog we often quote, "Algorithm = Logic + Control"; where we understand Logic to be the relationships that we've set up as "facts" in our Knowledge Base, and constraints over these relationships; and "Control" is the search procedure to resolve the queries over these facts and relationships. Having spent some years with this, I can say, there is a general way of doing computation, where your data model IS your computation; through stating the general relationship between entities in your data model, and constraints that must hold, you have the most declarative model. You only say WHAT; you let the resolution method (search) determine the HOW. I hope more people discover the "Blub" of decorativeness inherit in this model.

in 2005 was introduced to Kalido's Business Information modeling. Moved the bar very high, next 15 years I had to see corporations with lack of business modeling, and cascading issues in data ecosystem. This was before Data Governance was a major force. Came to same conclusion many ways - lack of business modeling in data costs us quite a bit.

“SQL is doesn’t tell you business intelligence” 5:45

Wow. First comment from Shanghai-based user 😃

Nice overview.

Related talks mentioned at 10:11: - KGC Bob Muglia: th-cam.com/video/Smbr-SW-fuQ/w-d-xo.html - CMU RelationalAI: th-cam.com/video/WRHy7M30mM4/w-d-xo.html - DSDSD Bravenboer: th-cam.com/video/KUf7xG3zGrI/w-d-xo.html

Brilliant ! Indeed "the model is the program " to be data driven.

I mean, is it REALLY a "normal" form if you don't tell us what it is ("secret sauce")? And if it's 6nf+, doesn't that introduce time/space complexity issues to actually achieve?

Isn't this just "depth first" traversals in a graph database?