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.

大量数据的可用性已在涉及基于计算机的关键系统的不同领域中广泛应用了机器学习和深度学习技术。大量的自动学习框架可以处理各种类型的系统，从而可以传播大数据分析，云计算系统和（工业）物联网。随着这样的应用变得越来越广泛，数据分析技术已经显示出它们能够识别操作模式并预测未来行为以预测可能出现的问题的能力。

通过高级推理机制（形式推理，模型检查，基于模型的方法）仍然难以操纵从这些方法中获得的知识：此特刊旨在探讨基于模型和数据驱动方法的协同作用，以促进关键应用程序的发展。以及系统分析和监控。