# Interoperable Data Analytics Reference Architectures Empowering Digital-Twin-Aided Manufacturing
## 1 Introduction  
Manufacturing is typically the backbone of every economy and is one of the oldest and most prominent economic sectors worldwide. In Europe, for example, manufacturing accounts for about 15% of the GDP and provides approximately 33 million jobs [1]. Additionally, it is widely estimated that every job in manufacturing creates up to two and a half other job opportunities across the value chain, further increasing the importance of the sector. Although manufacturing is one of the more traditional industries, it has undergone a significant transformation in the past decade. We are currently witnessing the so-called Industry 4.0 (I4.0) [2] revolution, where automated machines have been extended with cutting edge technologies such as smart sensors, intelligent robots, and innovative IT solutions to further improve productivity, increase automation, and improve the overall efficiency of processes. A central aspect of I4.0 is the concept of Digital Twins (DTs). A DT is typically a real-time digital model of a production line or industrial product that enables self-diagnosis, self-optimization, and self-configuration of the line without the need for human intervention. While there is no universally accepted definition of DTs, they typically represent bidirectional communication with a physical system, where information is received from the physical system, and new information is fed back to influence the operation of the production line. A Digital Twin is typically a complex application composed of a large number of components or services that are executed over a dispersed and heterogeneous set of resources, including both central cloud computing nodes and resources closer to the edge of the network and to IoT devices and sensors. The DIGITbrain project [3], funded by the European Commission's H2020 Programme, aims to make DTs more accessible to small and medium enterprises (SMEs)--who account for a large proportion of the Manufacturing industry [4] and have struggled to adopt DTs due to their inherent complexity [5]. To achieve this aim, DIGITbrain develops novel technologies that make the development and deployment of DTs easier, and therefore more affordable for smaller companies. Reference Architectures (RAs) are another concept that emerged recently in various domains, including both cloud computing environments and I4.0. An RA represents frequently emerging patterns and best practices that can be reused in various settings with relatively small customization effort. The main contribution of this paper is to explore and demonstrate how the concept of Reference Architectures can be effectively applied for the development and deployment of Digital Twins. According to our best knowledge, the combination of the two concepts has not yet been suggested and formalized. Utilizing the concept of RAs in the development and deployment of DTs has the potential of making the process faster and easier, without the need for specialized expertise in cloud or IoT technologies. Reference Architectures can reduce time and effort in designing and deploying complex applications such as DTs by encouraging re-usability and reducing the likelihood of errors. It will also be seen that Reference Architectures support and encourage the use of cloud orchestration tools to further empower application deployment and execution. To achieve this objective, we first overview related work and analyze the various forms of Reference Architectures to assess their relevance and applicability to Digital Twins and data analytics (in Section 2). After this, we define the generic core building blocks of Reference Architectures for DTs (with specific focus on data analytics applications), such as Software Architecture Components, Software Architecture Fragments, and Data Analytics Reference Architectures (in Section 3).