Automatic Rule Updating based on Machine Learning in Complex Event Processing

Abstract

Complex Event Process (CEP) is very essential in Semantic Web of Things (SWoT) that deploy a large number of sensor devices, like smart traffic and smart city. CEP mainly solves heterogenous problems of stream data processing, where streaming data is connected to internet by a mass of wireless sensor devices. The core work of CEP is rule updating. Existing researches of rule updating are designed for static environments, and it is quite laborious to transplant those rules for dynamic environments. To enhance the portability of event rules, a method of automatic rule updating based on machine learning is proposed to learn the rules of a dynamic environment. Experimental results reveal that the proposed methods are effective and efficient.