Speaker
Description
Modern cyber-physical system (CPS) design relies on the paradigm of component integration. Assurance of the compliance with extra-functional requirements of critical CPS applications necessitates empirical identification before integrating components, and validation during the final acceptance test and operation, respectively.
Benchmarking and operational log analysis are the primary means of validation and checking of time-related attributes, like timeliness or performability. These processes generate hard-to-interpret, many-dimensional, and big data sets. Exploratory Data Analysis (EDA) supports a better understanding of the collected data by integrating the priory knowledge of the domain expert with the observations. It extracts a model of the phenomena used later in system engineering and operational supervision of the CPS.
The talk presents a novel combination of discretization, knowledge fusion, inductive logic-based automated system model identification for validating, and diagnosing complex systems. It uses a sequence of steps of quantization, knowledge base construction by merging priory knowledge and observations and inductive logic-based reasoning for generalization of the observed results for automated model extraction, consistency checking, and fault diagnosis.
