Harnessing Community Knowledge in Heterogeneous Rule Engines

Currently, there is a lack of rule-based approaches that offer rich semantics that developers can use to exploit community knowledge contributed by distributed heterogeneous clients. Such abstractions can be useful in a number of applications to deal with the problem of orchestrating data patterns in a heterogeneous setting. This work presents scope-based reasoning in heterogeneous rule engines as a means to capture collective intelligence via community knowledge. Using scoped rules, rule designers can detect patterns in real-time data and to realise grouping structures in heterogeneous applications backed by a common rule-based system. The proposed solution exploits the fact that much of the heterogeneous community knowledge significant when performing reasoning and deductions can be structured hierarchically. We evaluate our work through a simulated case study, confirming that our technique presents a viable approach for efficiently processing community knowledge in heterogeneous environments.