Data and Visual Analytics for Cyber-physical Systems: Current Situation and Strategies for Action

Today, cyber-physical systems (CPS) exist everywhere in different sizes, with different functionalities and capabilities. CPS often support critical missions that have significant economic and societal importance. They require software systems, communications technologies, sensors/actuators, embedded technologies, and physical systems to work together seamlessly, and they are seen as a driving force behind digital transformation. This dissertation describes the research work carried out to investigate applicability of data and visual analytics for CPS to overcome three main challenges: interoperability, complexity, and sustainability. To this end, several case studies are used to effectively implement and test different data and visual analytics solutions to aid stakeholders when they make decisions on interoperability, complexity, and sustainability for CPS. These studies raised questions about issues found to be of importance for the success of data and visual analytics approaches, including accessibility, availability, quality, volume, and variety of data—issues. Moreover, additional studies are used to show the benefits of blending different approaches, such as systems thinking and design thinking, and the current data analytics readiness of the Swedish industry is assessed through a questionnaire completed by more than a hundred respondents. The data and visual analytics are positioned between digitalization and machine intelligence as a research focus. Data and visual analytics is the next step after digitalizing the information by adding analytical capabilities to the data. It is also an important phase before developing machine intelligence applications. Earlier studies clearly show that only a fraction of companies have machine intelligence applications across the enterprise. One important reason behind this is the lack of strong digital capabilities that big data and advanced data analytics technologies could bring. The findings of the work carried out as part of this thesis show the importance of this middle phase—data and visual analytics—for the success of not only the CPS but also these two concepts—digitalization and machine intelligence. This thesis concludes by highlighting that currentdata and visual analytics approaches in CPS are closely dependent onthe availability, accessibility, quality, volume, and variety of the data. Notably, the huge amount of industrial data that exists in CPS manufacturers data repositories does not always mea