An efficient MapReduce-based rule matching method for production system

Production systems based on knowledge rules have been widely used for reasoning both in industry and academia. However, rule matching in production system is time-consuming too much and it always incur the system crash when the massive knowledge exceeds the limitations of memory and computing capacity of one single computer. The advent of cloud computing-a new on-demand computing model brings us an inspiring perspective to address this problem. In this paper, a MapReduce-based rule matching method was proposed. It decomposes the task of rule matching and maps subtasks to different computers in a distributed and parallel computing environment, and gets the final matching result after reduce phase. An experimental evaluation shows the high efficiency of the method. A MapReduce based architecture for production system and its prototype implementation are presented and studied.Task allocation strategies including sub rules and facts are presented.Rule decomposition for map phase is studied.Redundant mechanism is introduced for credibility and stability.