  In this paper, we tackle the incremental maintenance of Datalog inference
materialisation when the rule set can be updated. This is particularly relevant
in the context of the Internet of Things and Edge computing where smart devices
may need to reason over newly acquired knowledge represented as Datalog rules.
Our solution is based on an adaptation of a stratification strategy applied to
a dependency hypergraph whose nodes correspond to rule sets in a Datalog
program. Our implementation supports recursive rules containing both negation
and aggregation. We demonstrate the effectiveness of our system on real and
synthetic data.
