Efficient Ontology-Based Modeling of Context-Aware In-Car Infotainment Systems - Benchmark Infrastructure and Design Guidelines

Context-aware systems, such as in-car infotainment systems, aim at improving the interaction between computers and humans by using contextual information about the system, the user and the environment. Previous work has shown that ontologies have significant benefits for modeling such context-aware systems, e.g., when inferring knowledge. However, the potential performance impact on the overall system when adopting ontologies, especially with rules, to model context-awareness is yet unknown. In this paper, we introduce a benchmark infrastructure and perform benchmarks on multiple ontologies of context-aware systems in order to determine factors that influence performance. From the results of these benchmarks, we derive guidelines for designing ontologies with rules for context-aware systems. These guidelines allow making conscious decisions about performance trade-offs and, in consequence, may improve suitability of ontologies for use in implementing industrial context-aware systems as they guide the creation of high-performance ontology-based context-aware systems.