Datalog has grown in popularity with its integration into various commercial and open-source systems. One significant application of Datalog is in stream reasoning scenarios, particularly in the context of Internet of Things and Edge Computing, where it supports the derivation of implicit consequences from incomplete streaming data. The dynamic nature of edge infrastructure, with constantly changing data and deduction rules, poses additional challenges for incremental reasoning. This paper introduces Zodiac, a method for reasoning under rule amendments, and ZodiacEdge, a system implementing this method. Zodiac is based on Datalog stratification and semi-naive evaluation but includes a novel data structure that supports incremental maintenance of deductions for fast rule amendments, even in the presence of negation and aggregation. ZodiacEdge is designed to work with RDF data, accepting Datalog programs with predicates of a maximum arity of two.

International audience

As edge computing is becoming a new platform for rich applications and services, it becomes more and more important to design adapted data management systems for this environment. In this paper, we present a prototype corresponding to a compact, in-memory RDF store that can answer SPARQL queries requiring reasoning services without necessitating any decompression. This demonstration highlights a design based on succinct data structures, shows some implementation details and provides encouraging performance measures over a set of real-world and synthetic data and query sets.