Convergence of Datalog over (Pre-) Semirings
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Abstract (arXiv)
Recursive queries have been traditionally studied in the framework of datalog, a language that restricts recursion to monotone queries over sets, which is guaranteed to converge in polynomial time in the size of the input. But modern big data systems require recursive computations beyond the Boolean space. In this paper we study the convergence of datalog when it is interpreted over an arbitrary semiring. We consider an ordered semiring, define the semantics of a datalog program as a least fixpoint in this semiring, and study the number of steps required to reach that fixpoint, if ever. We identify algebraic properties of the semiring that correspond to certain convergence properties of datalog programs. Finally, we describe a class of ordered semirings on which one can use the semi-na"ive evaluation algorithm on any datalog program.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 May 2021.
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Citing papers per year: 2023: 2; 2024: 0; 2025: 1; 2026: 2.
5 papers.
- 2023Scallop: A Language for Neurosymbolic Programmingcited by 63
- 2023The Complexity of Why-Provenance for Datalog Queriescited by 2
- 2026Recursive querying of neural networks via weighted structurescited by 2
- 2025A Unifying Framework for Semiring-Based Constraint Logic Programming With Negation (full version)cited by 0
- 2026PAGR: Proof-Carrying Algebraic-Geometric Retrieval: A Quiver-, Provenance-, and Sheaf-Theoretic Framework for Grounded LLM Retrievalcited by 0
- 2026PAGR: Proof-Carrying Algebraic-Geometric Retrieval: A Quiver-, Provenance-, and Sheaf-Theoretic Framework for Grounded LLM Retrieval5 Sep 2026
- 2026Recursive querying of neural networks via weighted structures6 Jan 2026
- 2025A Unifying Framework for Semiring-Based Constraint Logic Programming With Negation (full version)21 Jul 2025
- 2023Scallop: A Language for Neurosymbolic Programming10 Apr 2023
- 2023The Complexity of Why-Provenance for Datalog Queries22 Mar 2023