Learning frequent and periodic usage patterns in smart homes

This paper discusses how usage patterns and preferences of smart home inhabitants can be learned efficiently. Such patterns as a baseline of what constitutes normal behavior of inhabitants allows future smart homes to autonomously achieve positive effects like comfort increases, energy savings or improved safety for elderly residents in assisted living homes. The approach for learning usage patterns chosen by this research project, which was carried out as a Master Thesis at FHNW, uses frequent sequential pattern mining algorithms to mine the event data available in smart homes. While other authors have already published possible solutions or at least approaches to the problem, the information presented herein is unique because it is based solely on real-life smart home event data and not data collected in a laboratory trial and/or enriched by additional sensors. Furthermore the project does not only propose one solution but compares the performance of different algorithms regarding completeness/correctness of the results, run times as well as memory consumption and elaborates on the shortcomings of the different solutions. To be able to solve the challenge of learning usage patterns, this project followed the research onion framework by Saunders, et al. (2009) and the design science research paradigm by Vaishnavi & Kuechler (2004): after a research design and a literature review was done, the available secondary data was analyzed in depth before different solutions (including a brute-force algorithm specifically designed for this project as well as adaptations of the three established frequent sequential pattern mining algorithms PrefixSpan, BIDE+ and GapBIDE) were designed, implemented as prototypes in Java and benchmarked against each other....