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A distributed architecture for rule engine to deal with big data

机译：规则引擎的分布式架构，用于处理大数据

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摘要

Rule engine, which acknowledges 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. As traditional MapReduce architecture can only address this problem in certain conditions, we have made some improvements and therefore proposed a distributed implementation of the rule engine using MapReduce-based architecture. It is designed to deal with a large amount of data in a parallel and distributed way by using a computing cluster that consists of multiple machines, on which certain part of the Rete algorithm would be operated. In the phase of splitting rules and the Rete-net, Apriori algorithm is also improved and adopted so as to gain a better system performance. This paper not only describes details of the design and its implementation, but also shows its high performance through several experiments.
机译：规则引擎通过将事实与规则反复匹配来确认事实并得出结论，是一种知识表示和推理的好方法。但是，由于计算效率低和单机容量的限制，因此无法很好地处理大数据。由于传统的MapReduce架构只能在特定条件下解决此问题，因此我们进行了一些改进，因此提出了使用基于MapReduce的架构的规则引擎的分布式实现。它被设计为通过使用由多台计算机组成的计算集群以并行和分布式的方式处理大量数据，在该计算机上将运行Rete算法的某些部分。在分割规则和Rete-net阶段，还对Apriori算法进行了改进和采用，以获得更好的系统性能。本文不仅描述了该设计的细节及其实现，而且还通过几次实验展示了其高性能。

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