  Concept drift detection is crucial for many AI systems to ensure the system's
reliability. These systems often have to deal with large amounts of data or
react in real-time. Thus, drift detectors must meet computational requirements
or constraints with a comprehensive performance evaluation. However, so far,
the focus of developing drift detectors is on inference quality, e.g. accuracy,
but not on computational performance, such as runtime. Many of the previous
works consider computational performance only as a secondary objective and do
not have a benchmark for such evaluation. Hence, we propose and explain
performance engineering for unsupervised concept drift detection that reflects
on computational complexities, benchmarking, and performance analysis. We
provide the computational complexities of existing unsupervised drift detectors
and discuss why further computational performance investigations are required.
Hence, we state and substantiate the aspects of a benchmark for unsupervised
drift detection reflecting on inference quality and computational performance.
Furthermore, we demonstrate performance analysis practices that have proven
their effectiveness in High-Performance Computing, by tracing two drift
detectors and displaying their performance data.
