hode VS logica

I first heard of hypergraphs in the context of knowledge management using hode:

https://github.com/JeffreyBenjaminBrown/hode

I haven't looked more into them, but thought others might find hode interesting.

It's an interesting idea. Maybe I should try. I suspect, though, that I want control over every node, no matter how general. The thing about my notes is they contain only information I think I might need. I have, for instance, an "emotions" node. I'm sure the Wikidata node for emotions is interesting, but I want my own node too.

I tried to square this circle once by writing Hode[1], which permits encoding certain data as relevant to me, and then filter my view accordingly when I wanted. But the encoding process (i.e. the user experience when adding data to one's knowledge base) was so hard that I gave up.

[1] https://github.com/JeffreyBenjaminBrown/hode

I wrote a language that's very close to natural language for writing to and querying a RSLT, a kind of higher order graph.

https://github.com/JeffreyBenjaminBrown/hode/blob/master/doc...

I know there are people with money who want to save the world. I think a higher-order knowledge base like Hode[1] could be useful in search, in AI, and in social networking. But I have no idea how to monetize it. It's more of a science project than a business. Am I out of luck until I do have such idea?

[1] https://github.com/JeffreyBenjaminBrown/hode

If you're interested in this I would also recommend you check out Logica[0], which is a datalog-like language that is explicitly made to compile to SQL queries.

0: https://logica.dev/

> I guess the intention is to be better than SQL but then I was left with "under which circumstances?"

Excellent question.

Two of the most common use cases for databases are "transactional processing" (manipulating small numbers of rows in real time) and "analytical processing" (querying enormous numbers of rows, typically in a read-only fashion).

SQL is generally fine for transactional workloads.

But analytical queries sometimes involve multi-page queries, with lots of JOINs and CTEs. And these queries are often automatically generated.

And once you start writing actual multi-page "programs" in SQL, you may decide that it's a fairly clunky and miserable programming language. What Datalog typically buys you is a way to cleanly decompose large queries into "subroutines." And it offers a simpler syntax for many kinds of complex JOINs.

Unfortunately, there isn't really a standard dialect of Datalog, or even a particular dialect with mainstream traction. So choosing Datalog is a bit of a tradeoff: does it buy you enough, for your use case, that it's worth being a bit outside the mainstream? Maybe! But I'd love to see something like Logica gain more traction: https://logica.dev/

Interesting; a Google engineer previously published a Datalog variant for BigQuery: https://logica.dev/

This new language seems similar to differential-Datalog (which is sadly in maintenance mode): https://news.ycombinator.com/item?id=33521561

Looks pretty cool. I'd be interested if the README had a comparison with Google's Logica (https://github.com/EvgSkv/logica)

Oh wow that is neat!

And yes, this kind of thing is why datalog is a lot more amenable to fast query plans & runtimes than prolog. This part is especially cool: https://github.com/EvgSkv/logica/blob/main/compiler/dialects...

Google new programming Language that compiles to SQL (Support BigQuery and Postgres) feels very exciting. Blog: https://opensource.googleblog.com/2021/04/logica-organizing-your-data-queries.html Github: https://github.com/EvgSkv/logica

Going to be? It already is. In fact, one thing the article misses is right there at the bottom of the project page: