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Conference Papers Year : 2022

Efficient Provenance-Aware Querying of Graph Databases with Datalog

Yann Ramusat
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Abstract

We establish a translation between a formalism for dynamic programming over hypergraphs and the computation of semiringbased provenance for Datalog programs. The benefit of this translation is a new method for computing the provenance of Datalog programs for specific classes of semirings, which we apply to provenance-aware querying of graph databases. Theoretical results 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, competing with dedicated solutions for graphs.
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Dates and versions

hal-03664928 , version 1 (11-05-2022)

Identifiers

  • HAL Id : hal-03664928 , version 1

Cite

Yann Ramusat, Silviu Maniu, Pierre Senellart. Efficient Provenance-Aware Querying of Graph Databases with Datalog. GRADES-NDA 2022 - Joint Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA), Jun 2022, Philadelphia, United States. ⟨hal-03664928⟩
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