crepe VS virtuoso-opensource

https://github.com/topics/datalog?l=rust ... Cozo, Crepe

Crepe: https://github.com/ekzhang/crepe :

> Crepe is a library that allows you to write declarative logic programs in Rust, with a Datalog-like syntax. It provides a procedural macro that generates efficient, safe code and interoperates seamlessly with Rust programs.

Looks like there's not yet a Python grammar for the treeedb tree-sitter: https://github.com/langston-barrett/treeedb :

> Generate Soufflé Datalog types, relations, and facts that represent ASTs from a variety of programming languages.

Looks like roxi supports n3, which adds `=>` "implies" to the Turtle lightweight RDF representation: https://github.com/pbonte/roxi

FWIW rdflib/owl-rl: https://owl-rl.readthedocs.io/en/latest/owlrl.html :

> simple forward chaining rules are used to extend (recursively) the incoming graph with all triples that the rule sets permit (ie, the “deductive closure” of the graph is computed).

ForwardChainingStore and BackwardChainingStore implementations w/ rdflib in Python: https://github.com/RDFLib/FuXi/issues/15

Fast CUDA hashmaps

Gdlog is built on CuCollections.

GPU HashMap libs to benchmark: Warpcore, CuCollections,

https://github.com/NVIDIA/cuCollections

https://github.com/NVIDIA/cccl

https://github.com/sleeepyjack/warpcore

/? Rocm HashMap

DeMoriarty/DOKsparse:

Inspired by past work: LINQ, inline-python, crepe, DataScript, Riffle.

https://en.wikipedia.org/wiki/Datalog#Evaluation

...

VMware/ddlog: Differential datalog

> Bottom-up: DDlog starts from a set of input facts and computes all possible derived facts by following user-defined rules, in a bottom-up fashion. In contrast, top-down engines are optimized to answer individual user queries without computing all possible facts ahead of time. For example, given a Datalog program that computes pairs of connected vertices in a graph, a bottom-up engine maintains the set of all such pairs. A top-down engine, on the other hand, is triggered by a user query to determine whether a pair of vertices is connected and handles the query by searching for a derivation chain back to ground facts. The bottom-up approach is preferable in applications where all derived facts must be computed ahead of time and in applications where the cost of initial computation is amortized across a large number of queries.

From https://community.openlinksw.com/t/virtuoso-openlink-reasoni... https://github.com/openlink/virtuoso-opensource/issues/660 :

> The Virtuoso built-in (rule sets) and custom inferencing and reasoning is backward chaining, where the inferred results are materialised at query runtime. This results in fewer physical triples having to exist in the database, saving space and ultimately cost of ownership, i.e., less physical resources are required, compared to forward chaining where the inferred data is pre-generated as physical triples, requiring more physical resources for hosting the data.

FWIU it's called ShaclSail, and there's a NotifyingSail: org.eclipse.rdf4j.sail.shacl.ShaclSail: https://rdf4j.org/javadoc/3.2.0/org/eclipse/rdf4j/sail/shacl...