DRESS: A Rule Engine on Spark for Event Stream Processing
Rule based systems process event streams and trigger actions according to pre defined rule sets. Over the last three decades, such systems have been widely used in businesses, governments and organisations. However, with today's need to process larger event streams such as events produced in Internet of Things (IoT) current rule based systems face serious challenges in terms of speed, scalability and fault tolerance. Spark Streaming is emerging as a novel solution to address these challenges. This paper presents an approach for adapting rule based systems to work with Spark Streaming. We focus on Rete algorithm which is behind many of the current rule engines. We present DRESS (Distributed Rule Engine on Spark Streaming) an infrastructure for executing Rete algorithm on Spark Streaming. In addition, we present an automated method of transforming rules written in Drools' style to be executed on DRESS. The performance of our system was evaluated with the help of a case study.
2016 IEEE/ACM 3rd International Conference on Big Data Computing Applications and Technologies (BDCAT)