一种分布式规则引擎的实现方法

A distributed Architecture for Rule Engine

规则引擎可以接受数据输入,解释业务规则,并根据业务规则做出业务决策,但是由于其只能使用在单机系统上的局限性,当其处理大量数据时,会显著的影响系统性能。为解决这个问题,本文在传统的Map Reduce框架下做出了改进,提出了一种分布式的规则引擎的实现方法。此方法通过构建一个并行的计算集群来处理大量的数据,集群中的每一台节点都有一个分支Rete网络。在规则分解和部署的过程中,利用了Apriori提高系统的性能。本文不仅在理论上描述了设计方法,而且也通过实验数据体现了系统的高性能。

Rule engine,which accepts the facts and draws conclusions by repeatedly matching facts with rules,is a good way of knowledge representation and inference.However,because of its low computational efficiency and the limitation of single machine's capacity,it cannot deal well with big data.Traditional MapReduce architecture can only address this problem in some special conditions,so we improve it and propose a distributed implementation of the rule engine using MapReduce-based architecture.It is designed to make use of a computing cluster which consist of multiple machines running part of Rete algorithm on each,to deal with a large amount of data in a parallel and distributed way.The Apriori algorithm is also modified and used,To gain a better system performance,on the stage of splitting rules and the Rete-net.This paper not only describes details of the design and implementation of it,but also shows its high performance through several experiments.