Mining sequential patterns of event streams in a smart home application

Recent advances in sensing techniques enabled the possibil- ity to gain precise information about switched-on devices in smart home environments. One is particularly interested in exploring different pat- terns of electrical usage of indoor appliances and using them to predict activities. This in turns results with many useful applications like in- ferring 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.