Ontology-Based Integration of Vehicle-Related Data

Vehicle architectures have evolved over the past two decades to provide support for data-driven functionalities. The typical approach in this domain has been application-centric, leading to data models that are disparate, repetitive, and hardly maintainable in the long run. As a result, the software complexity increases, while the knowledge remains hidden in the applications' code. We argue that it is essential to enrich the data with standard semantic models to enable a smooth integration of heterogeneous data. In this paper, we propose an ontology-based approach to integrate vehicle-related data. It consists of semantically annotating application-specific data with a well-defined semantic model that considers its streaming-nature. Three applications that use vehicle data are implemented and annotated with the presented procedure. The resulting semantic data is validated with elaborated analytical competency questions that combine application-specific data. Such questions are satisfied with the implementation of queries that follow the patterns of the semantic model. Our work shows that ontology-based data integration is a suitable component for vehicle architectures. The use of this type of integration implies the one-time implementation of queries that are stable over time, reusability of application-specific data, and increased semantics.