Context awareness and adaption are an important requirement for data streaming applications in the Internet of Things (IoT). Concept drift arises under changing context and many solutions for drift detection and system adaption have been developed in the past. Even though the computing resources of these approaches are not negligible, performance aspects, e.g. runtime or memory usage and scalability have not been investigated adequately. The goal of this thesis is to fill this gap and to provide a mean for performance investigations of concept drift handling methods and other approaches that are developed in the research field Data Science. Therefore performance benchmarking of state-of-the-art solutions is conducted. Moreover the thesis discusses performance bottlenecks of the approaches and demonstrates possible improvements based on the knowledge gained by leveraging tools and methods from the performance analysis domain. Approaches are then implemented into an IoT application and deployed with stream processing engines for a final evaluation.