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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.We also propose a recommender system based on the developed algorithm. This recommender provides recommendations to the users to reduce their energy consumption. The recommender system was deployed to a set of test homes. The test participants rated the impact of the recommendations on their comfort. We used this feedback to adjust the system parameters and make it more accurate during a second test phase. The historical dataset provided by digitalSTROM contained 33 homes with 3521 devices and over 4 million events. The system produced 160 recommendations on the first phase and 120 on the second phase. The ratio of useful recommendations was close to 10%.

Abstract. In many application scenarios, the use of the Web ontology language OWL is hampered by the complexity of the underlying logic that makes reasoning in OWL intractable in the worst case. In this paper, we address the question whether approximation techniques known from the knowledge representation literature can help to simplify OWL reasoning. In particular, we carry out experiments with approximate deduction techniques on the problem of classifying new concept expressions into an existing OWL ontology using existing Ontologies on the web. Our experiments show that a direct application of approximate deduction techniques as proposed in the literature in most cases does not lead to an improvement and that these methods also suffer from some fundamental problems.

This paper presents a case study of a recommender system that can be used to save energy in smart homes without lowering the comfort of the inhabitants. We present an algorithm that mines consumer behavior data only and applies machine learning to suggest actions for inhabitants to reduce the energy consumption of their homes. The system looks for frequent and periodic patterns in the event data provided by the digitalSTROM home automation system. These patterns are converted into association rules, prioritized and compared with the current behavior of the inhabitants. If the system detects opportunities to save energy without decreasing the comfort level, it sends a recommendation to the inhabitants.The system was implemented and deployed to a set of test homes. The test participants were able to rate the impact of the recommendations on their comfort. This feedback was used to adjust the system parameters and make it more accurate during a second test phase. The historical data set provided by digitalSTROM contained 33 homes with 3521 devices and over 4 million events. The system produced 160 recommendations on the first phase and 120 on the second phase. The ratio of useful recommendations was close to 10%. We found out that a recommender system that uses an algorithm that mines patterns based on their confidence, independent of their frequency and periodicity, might achieve better results and a higher acceptance by users.

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The extensive work on Knowledge Engineering in the 1990s has resulted in a systematic analysis of task-types, and the corresponding problem solving methods that can be deployed for different types of tasks. That analysis was the basis for a sound and widely accepted methodology for building knowledge-based systems, and has made it is possible to build libraries of reusable models, methods and code.In this paper, we make a first attempt at a similar analysis for Semantic Web applications. We will show that it is possible to identify a relatively small number of tasktypes, and that, somewhat surprisingly, a large set of Semantic Web applications can be described in this typology. Secondly, we show that it is possible to decompose these task-types into a small number of primitive ("atomic") inference steps. We give semi-formal definitions for both the task-types and the primitive inference steps that we identify. We substantiate our claim that our task-types are sufficient to cover the vast majority of Semantic Web applications by showing that all entries of the Semantic Web Challenges of the last 3 years can be classified in these task-types.