The development of (semi)-autonomous vehicles requires extensive communication between vehicles and the infrastructure called V2X communication. This should allow to increase road safety, which is a major objective of Cooperative Intelligent Transport Systems (C-ITS), and can be achieved by analyzing traffic scenes in real-time and detecting events that could lead to accidents, e.g., red light violations [2] . Roadside C-ITS stations will support V2X communication with cars and the

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... e such as traffic lights, but also could be extended for more complex tasks as traffic scene analysis. We illustrate the necessity of analyzing traffic scenes by two real-world scenarios, which are known problems in the field of C-ITS regarding safety and optimization [2] . The first scenario, called road intersection safety, was identified in [2] , where the authors consider "road intersection monitoring" as an important application to improve road safety. For this scenario, we assume a complex intersection with a sensor-based roadside C-ITS stations, where traffic accidents happen frequently. The second scenario, called changing traffic situations, concerns the deployment and maintenance of these stations. Currently, a roadside station is configured once at deployment, hence it cannot react dynamically on a changing environment such as road construction or traffic jams due to a misconfiguration of signal phases. Smart roadside stations could become autonomous by dynamically adapting to the changing environment and traffic situations. For instance, they could recognize unoptimized signal phases and adjust the phases according to the new situation. Enabling the analysis of traffic scenes as in the above scenarios, includes more general characteristics such as dealing with the complex C-ITS domain, as well as handling large quantities of message-based and spatio-temporal data. This characteristics are not only relevant fo the C-ITS domain, but also exits in other fields such as robotics or geospatial analysis. Primarily, we have identified two different abstract levels of understanding for the analysis, each of them poses its own challenges as: 1. How do we efficiently analyze C-ITS streams for detecting short-term problems as (complex) events, e.g., accident detection; * This thesis research is conducted within the project LocTraffLog (http://www.kr.tuwien.ac.at/research/projects/loctrafflog/) funded by the industrial PhD program of the FFG, in cooperation with Siemens AG Austria.