(2015) Schweizer, Daniel; Zehnder, Michael; Wache, Holger; Zanatta, Danilo; Rodriguez, Miguel; Witschel, Hans Friedrich
This paper discusses how usage patterns and preferences of inhabitants can be learned efficiently to allow smart homes to autonomously achieve energy savings. We propose a frequent sequential pattern mining algorithm suitable
for real-life smart home event data. The performance of the proposed algorithm is compared to existing algorithms regarding
completeness/correctness of the results, run times as well as memory consumption and elaborates on the shortcomings of the different solutions.