Detecting Mobility Patterns using Spatial Query Answering over Streams
The development of (semi)-autonomous vehicles and communication between vehicles and infrastructure (V2X) will aid to improve road safety by identifying dangerous traffic scenes. A key to this is the Local Dynamic Map (LDM), which acts as an integration platform for static, semi-static, and dynamic information about traffic in a geographical context. At present, the LDM approach is purely database-oriented with simple query capabilities, while an elaborate domain model as captured by an ontology and queries over data streams that allow for semantic concepts and spatial relationships are still missing. To fill this gap, we present an approach in the context of ontology-mediated query answering that features conjunctive queries over DL-LiteA ontologies allowing spatial relations and window operators over streams having a pulse. For query evaluation, we present a rewriting approach to ordinary DL-LiteA that transforms spatial relations involving epistemic aggregate queries and uses a decomposition approach that generates a query execution plan. Finally, we report on experiments with two scenarios and evaluate our implementation based on the stream RDBMS PipelineDB.