ascent
virtuoso-opensource
ascent | virtuoso-opensource | |
---|---|---|
4 | 1 | |
370 | 844 | |
- | - | |
6.3 | 8.9 | |
19 days ago | 25 days ago | |
Rust | C | |
MIT License | GNU General Public License v3.0 or later |
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ascent
- Datalog in 100 lines of JavaScript (2022)
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Databases are the endgame for data-oriented design
A datalog engine like https://github.com/s-arash/ascent is worth looking at.
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GDlog: A GPU-Accelerated Deductive Engine
Sounds awesome--feel free to get in touch with us (the authors of this paper) and share your progress. We have a similar single-node Datalog engine in Rust, it would be cool to benchmark your results against parallel Ascent (https://github.com/s-arash/ascent).
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Datafrog: A lightweight Datalog engine in Rust
I think people should look at Ascent [1]. I love the way it embeds Rust seamlessly. It's like JSX for Datalog/Rust.
[1]: https://github.com/s-arash/ascent
virtuoso-opensource
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GDlog: A GPU-Accelerated Deductive Engine
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...
What are some alternatives?
datafrog - A lightweight Datalog engine in Rust
treeedb - Generate Soufflé Datalog types, relations, and facts that represent ASTs from a variety of programming languages.
leanstore
FuXi - Chimezie Ogbuji's FuXi reasoner. NON-FUNCTIONING, RETAINED FOR ARCHIVAL PURPOSES. For working code plus version and associated support requirements see:
warpcore - A Library for fast Hash Tables on GPUs
pydatalog - Fork of pyDatalog https://sites.google.com/site/pydatalog/
jsoncrack.com - ✨ Innovative and open-source visualization application that transforms various data formats, such as JSON, YAML, XML, CSV and more, into interactive graphs.
highway - Performance-portable, length-agnostic SIMD with runtime dispatch
cccl - CUDA C++ Core Libraries
roxi - Reactive Reasoning
NMT4RDFS - Neural Machine Translation for RDFS reasoning: code and datasets for "Deep learning for noise-tolerant RDFS reasoning" http://www.semantic-web-journal.net/content/deep-learning-noise-tolerant-rdfs-reasoning-4