Advancements in knowledge elicitation for computer-based critical systems

Abstract The availability of a huge amount of data has enabled the massive application of machine learning and deep learning techniques across different domains involving computer-based critical systems. A huge set of automatic learning frameworks tackle different kinds of systems, enabling the diffusion of Big Data analysis, cloud computing systems and (Industrial) Internet of Things. As such applications become more and more widespread, data analysis techniques have shown their capability to identify operational patterns and to predict future behaviours for anticipating possible problems. Knowledge outcoming from these approaches are still hard to manipulate with high-level reasoning mechanisms (formal reasoning, model checking, model-based approaches): this special issue aims at exploring the synergy of model-based and data-driven approaches to boost critical applications and systems analysis and monitoring.