Modern industrial and infrastructure systems have a cyber-physical architecture, incorporating the underlying physical processes regulated by a control cyber system. In other words, they represent interdependent networks described by disparate mathematical models creating scientific challenges that go well beyond the modeling and analysis of the individual system layers. This calls for a development of novel data mining and machine learning approaches for the implementation, operation, data analytics, contol, and optimization in real-world infrastructure systems. Moreover, this complex structure is at the origin of vulnerabilities of the system to internal and external failures, as well as to the cyber attacks. Therefore, the questions of development of adequate models and methods for an efficient detection and localization of intrusions and faults, as well as proportional responses to potential attacks is of prime significance in many industrial systems, such as automotive systems, smart manufacturing, power grid, HVAC and building systems, etc.

Given that the detailed information about the underlying system topology and interactions might not be available, the emerging data mining techniques are becoming fundamental to tackle the aforementioned challenges. These techniques requires ideas and methodology from a wide variety of fields, including but not limited to statistical modeling, graph theory, nomaly detection, optimization, machine learning, time series analytics, etc. Focusing on the methodological and practical aspects of data mining for industrial systems, this workshop provides an opportunity to discuss the latest theoretical advances and real-world applications in the field of cyber-physical systems.