Fixpoints for the Masses: Programming with First-Class Datalog Constraints скачать в хорошем качестве

Fixpoints for the Masses: Programming with First-Class Datalog Constraints

In "Fixpoints for the Masses: Programming with First-Class Datalog Constraints", we present how we have embedded Datalog in a functional programming language enabling a blend of functional and logic programming. Fixpoints for the Masses: Programming with First-Class Datalog Constraints Paper DOI: https://doi.org/10.1145/3428193 Presented at OOPSLA, part of SPLASH 2020 By Magnus Madsen, Ondřej Lhoták Datalog is a declarative logic programming language that has been used in a variety of applications, including big-data analytics, language processing, networking and distributed systems, and program analysis. In this paper, we propose first-class Datalog constraints as a mechanism to construct, compose, and solve Datalog programs at run time. The benefits are twofold: We gain the full power of a functional programming language to operate on Datalog constraints-as-values, while simultaneously we can use Datalog where it really shines: to declaratively express and solve fixpoint problems. We present an extension of the lambda calculus with first-class Datalog constraints, including its semantics and a type system with row polymorphism based on Hindley-Milner. We prove soundness of the type system and implement it as an extension of the Flix programming language.