A Novel Approach to Distributed Rule Matching and Multiple Firing Based on MapReduce

Tianyang Dong, Qiang Cheng, Bin Cao, Jianwei Shi

OAI: oai:igi-global.com:211915 • DOI: 10.4018/JDM.2018040104

In order to solve the poor performance problem of massive rules reasoning, as well as the inconsistency problem of working memory in distributed rule matching, this article presents the formal definition of interference relations between rules, and proposes a novel approach to distributed rule matching and multiple firing based on MapReduce. This approach adopts the way of access request control to detect and exclude interference rules, then selects several rule instantiations to perform multiple firing and concurrent execution, thus reducing the number of inference cycles effectively. By detecting the interferences between rules, this method selects and executes compatible rule sets, and avoids the inconsistency problem of system working memory. In order to verify the validity of the authors' approach, this article developes a production system based on MapReduce, and applied this approach in the master server of a distributed production system. The experimental results show that their method can promote the performance of massive rules reasoning effectively.

Databases Computer Science and Information Technology Data Mining and Databases