In complex distributed systems, the importance of empirical data analysis-based testing, verification, and validation increases to assure a proper level of service under the typically varying workload. Scaling of these systems needs reusable and scale-independent models for reconfigurability. The limited faithfulness of speculative analytic models does not support complex system identification. This way, empirical system identification from observations is emerging in this field. The increasing complexity necessitates explainable and well-interpretable models that follow the logic of everyday thinking to validate the model and its use in operation. Qualitative modeling represents and reasons about human-understandable symbolic, formalized, discrete abstractions of continuous temporal and magnitude aspects of system behavior. Our research focuses on empirical system engineering by extracting qualitative models from observations (e.g., benchmarks, operation logs) assisted by a combination of exploratory and confirmatory data analysis. Extracted models can form the core of supervisory control (e.g., digital twins) or diagnosis.