Mobius: Synthesizing Relational Queries with Recursive and Invented Predicates

Synthesizing relational queries from data is challenging in the presence of recursion and invented predicates. We propose a fully automated approach to synthesize such queries. Our approach comprises of two steps: it first synthesizes a non-recursive query consistent with the given data, and then id...

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Vydáno v:Proceedings of ACM on programming languages Ročník 7; číslo OOPSLA2; s. 1394 - 1417
Hlavní autoři: Thakkar, Aalok, Sands, Nathaniel, Petrou, George, Alur, Rajeev, Naik, Mayur, Raghothaman, Mukund
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, NY, USA ACM 16.10.2023
Témata:
Automatic programming
Constraint and logic programming
Information systems
Programming by example
Relational database query languages
Software and its engineering
Theory of computation
example-guided synthesis
recursive program synthesis
Programming by example
ISSN:2475-1421, 2475-1421
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Shrnutí:Synthesizing relational queries from data is challenging in the presence of recursion and invented predicates. We propose a fully automated approach to synthesize such queries. Our approach comprises of two steps: it first synthesizes a non-recursive query consistent with the given data, and then identifies recursion schemes in it and thereby generalizes to arbitrary data. This generalization is achieved by an iterative predicate unification procedure which exploits the notion of data provenance to accelerate convergence. In each iteration of the procedure, a constraint solver proposes a candidate query, and a query evaluator checks if the proposed program is consistent with the given data. The data provenance for a failed query allows us to construct additional constraints for the constraint solver and refine the search. We have implemented our approach in a tool named Mobius. On a suite of 21 challenging recursive query synthesis tasks, Mobius outperforms three state-of-the-art baselines Gensynth, ILASP, and Popper, both in terms of runtime and accuracy. We also demonstrate that the synthesized queries generalize well to unseen data.
ISSN:2475-1421
2475-1421
DOI:10.1145/3622847