A Practical Dynamic Programming Approach to Datalog Provenance Computation

Abstract

We establish a translation between a formalism for dynamic programming over hypergraphs and the computation of semiring-based provenance for Datalog programs. The benefit of this translation is a new method for computing provenance for a specific class of semirings. Theoretical and practical optimizations lead to an efficient implementation using Soufflé, a state-of-the-art Datalog interpreter. Experimental results on real-world data suggest this approach to be efficient in practical contexts, even competing with our previous dedicated solutions for computing provenance in annotated graph databases. The cost overhead compared to plain Datalog evaluation is fairly moderate in many cases of interest.

Using Provenance in Data Analytics for Seismology: Challenges and Directions

“…the original query, competing with state-ofthe-art solutions for how-provenance computation. The work in [9] establishes a translation between a formalism for dynamic programming over hypergraphs and the computation of semiring-based provenance for Datalog programs. The approach proposes a new method for computing provenance for a specific class of semirings.…”