Parallel Collaborative Reasoning Approaches Based on DatalogMTL in IoT Scenarios

An important task in IoT application scenarios is to perform synergy reasoning on the phenomenal data and events of strong temporal semantics and complex correlations characteristics, with the help of associated knowledge and rules. However, current reasoning methods suffer from difficult rule representation with poor readability, lack of temporal semantics, and high reasoning complexity and inefficiency. To address these problems, this paper proposes parallel collaborative reasoning approaches based on DatalogMTL. Firstly, a series of collaborative access control mechanisms are designed for the concurrent conflict problems. Then, the rule-level parallel and fact-level parallel reasoning methods are presented based on materialization algorithm respectively. In this paper, we take experiments on two relative datasets and verify that our approaches greatly improve the reasoning efficiency and have good scalability in IoT scenarios.