A Scalable Complex Event Analytical System with Incremental Episode Mining over Data Streams

Abstract

Episode pattern mining is a very powerful technique to get high-valued information for people to solve real-life cross-disciplinary problems, such as for the analysis of manufacturing, stock markets, weather records and so on. As data grows, the mining process must be re-triggered again and again to obtain the most updated information. However, periodically re-mining the full dataset is not cost-effective, and thus a number of incremental mining approaches arise for the growing data. However, to our best knowledge, there exist few studies targeted on the problem of incremental episode mining. Moreover, streaming data of complex events is more and more popular because digital sensors always collect data around us in this big data age. Now the challenge is not only mining valuable episode patterns of incremental dataset, but also mining episode patterns over data streams of complex events. To address this research problem, we adopt the Lambda Architecture to design a scalable complex event analytical system that could be used to facilitate the incremental episode mining process over complex event sequences of data streams. Apache Spark and Apache Spark Streaming are applied as the development framework of the batch layer and the speed layer, respectively. To take both the efficiency and accuracy into consideration, we develop a series of modules and three algorithms, namely, batch episode mining, delta episode mining and pattern merging. Results from the experimental validation on a real dataset show that the proposed system carries high scalability and delivers excellent performance in terms of efficiency and accuracy.