ZodiacEdge: a Datalog Engine With Incremental Rule Set Maintenance
This solves the problem of efficient rule set updates in Datalog for smart devices in IoT and Edge computing, but it is incremental as it adapts existing stratification strategies.
The paper addresses incremental maintenance of Datalog inference materialization when rule sets are updated, particularly for IoT and Edge computing, and demonstrates effectiveness on real and synthetic data.
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.