A Workflow Framework for Big Data Analytics: Event Recognition in a Building

This paper studies event recognition in a building based on the patterns of power consumption. It is a big challenge to identify what kinds of events happened in a building without additional devices such as camera and motion sensors, etc. Instead, we learn when and how the events happened from the historical record of power consumption and apply the lesson into the design of an event recognition system (ERS). The ERS will find out abnormal power usage to avoid wasting power, which leads to the energy savings in a building. The ERS involves big data analytics with a large size of dataset collected in a real time. Such a data intensive system is usually viewed as a workflow. A workflow management is a significant task of the system requiring data analysis in terms of the system scalability to maintain high throughput or fast speed analysis. We propose a workflow framework that allows users to perform remote and parallel workflow execution, whose tasks are efficiently scheduled and distributed in cloud computing environment. We run the ERS as a target system for the proposed framework with power consumption data (whose size is approximately 20GB or more) collected from each of over 240 rooms in a building at Dept. of Engineering, Tokyo University in 2011. We show that the proposed framework accelerates the speed of data analysis by providing scaling infrastructure and parallel processing feature utilizing cloud computing technologies. We also share our experience and results on the big data analytics and discuss how the studies contribute to achieve Green Campus.