Empirical Systems Engineering and Modeling

Key techniques of Exploratory Data Analysis (EDA) and Confirmatory Data Analysis for deriving phenomenological models from observations.

Basics of hybrid modeling, discretization techniques and the continuous-discrete model transition. Basics of qualitative modeling, statistical validation of basic properties. Mathematical handling of qualitative models.

The basics of rough set theory, its applications in modeling for dependability assurance, when only partial information/knowledge is available.

Answer set programming and its application for approximative modeling and diagnosis. Model validation.

Representation of complex models as knowledge graphs, capturing a priori knowledge in knowledge graphs, consistency checking of observation-derived data and a priori knowledge.

Model identification case studies (dependable and resilient IT systems).

The role and application of empirically derived models in modern system design and operation. Key processes (e.g., modern capacity planning, chaos engineering, ...); the Digital Twin paradigm; knowledge bases of self-* processes (from Event-Condition-Action models to semantic reasoning support).

Outlook: protections against model errors, continuous model reassessment.