Mining sequential patterns of event streams in a smart home application

Abstract

Recent advances in sensing techniques enabled the possibility to gain precise information about switched-on devices in smart home environments. One is particularly interested in exploring different patterns of electrical usage of indoor appliances and using them to predict activities. This in turns results with many useful applications like inferring effective energy saving procedures. The necessity to derive this knowledge in the real time and the huge size of generated data initiated the need for a precise stream sequential pattern mining approach. Most available approaches are less accurate due to their batch-based nature. We present a smart home application of the PBuilder algorithm which uses a batch-free approach to mine sequential patterns of a real dataset collected from appliances. Additionally, we present the StrPMiner which uses the PBuilder to find sequential patterns within multiple streams. We show through an extensive evaluation over a smart home real dataset the superiority of the StrPMiner algorithm over a state-of-the-art approach.