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Scalable Process Monitoring through Rules and Neural Networks

In this paper we introduce RuleRunner, a Runtime Verification system for monitoring LTL properties over finite traces. By exploiting results from the Neural-Symbolic Integration area, a RuleRunner monitor can be encoded in a recurrent neural network. The results show that neural networks can perform real-time runtime verification and techniques of parallel computing can be applied to improve the performance in terms of scalability. Furthermore, our framework allows for property adaptation by using a standard neural network learning algorithm.