DSL-based Interoperability and Integration in the Smart Manufacturing Digital Thread

In the industry 4.0 ecosystem, a Digital Thread connects the data and processes for smarter manufacturing. It provides an end to end integration of the various digital entities thus fostering interoperability, with the aim to design and deliver complex and heterogeneous interconnected systems. We develop a service oriented domain specific Digital Thread platform in a Smart Manufacturing research and prototyping context. We address the principles, architecture and individual aspects of a growing Digital Thread platform. It conforms to the best practices of coordination languages, integration and interoperability of external services from various platforms, and provides orchestration in a formal methods based, low-code and graphical model driven fashion. We chose the Cinco products DIME and Pyrus as the underlying IT platforms for our Digital Thread solution to serve the needs of the applications addressed: manufacturing analytics and predictive maintenance are in fact core capabilities for the success of smart manufacturing operations. In this regard, we extend the capabilities of these two platforms in the vertical domains of data persistence, IoT connectivity and analytics, to support the basic operations of smart manufacturing. External native DSLs provide the data and capability integrations through families of SIBs. The small examples constitute blueprints for the methodology, addressing the knowledge, terminology and concerns of domain stakeholders. Over time, we expect reuse to increase, reducing the new integration and development effort to a progressively smaller portion of the models and code needed for at least the most standard application

Efficient Model-Driven Prototyping for Edge Analytics

Software development cycles in the context of IoT! (IoT!) applications require the orchestration of different technological layers, and involve complex technical challenges. The engineering team needs to become experts in these technologies and time delays are inherent due to the cross-integration process because they face steep learning curves in several technologies, which leads to cost issues, and often to a resulting product that is prone to bugs. We propose a more straightforward approach to the construction of high-quality IoT applications by adopting model-driven technologies (DIME and Pyrus), that may be used jointly or in isolation. The presented use case connects various technologies: the application interacts through the EdgeX middleware platform with several sensors and data analytics pipelines. This web-based control application collects, processes and displays key information about the state of the edge data capture and computing that enables quick strategic decision-making. In the presented case study of a Stable Storage Facility (SSF), we use DIME to design the application for IoT connectivity and the edge aspects, MongoDB for storage and Pyrus to implement no-code data analytics in Python. We have integrated nine independent technologies in two distinct Low-code development environments with the production of seven processes and pipelines, and the definition of 25 SIBs in nine distinct DSLs. The presented case study is benchmarked with the platform to showcase the role of code generation and the reusability of components across applications. We demonstrate that the approach embraces a high level of reusability and facilitates domain engineers to create IoT applications in a low-code fashion

The interoperability challenge: building a model-driven digital thread platform for CPS

With the heterogeneity of the industry 4.0 world, and more generally of the Cyberphysical Systems realm, the quest towards a plat form approach to solve the interoperability problem is front and centre to any system and system-of-systems project. Traditional approaches cover individual aspects, like data exchange formats and published interfaces. They may adhere to some standard, however they hardly cover the pro duction of the integration layer, which is implemented as bespoke glue code that is hard to produce and even harder to maintain. Therefore, the traditional integration approach often leads to poor code quality, further increasing the time and cost and reducing the agility, and a high reliance on the individual development skills. We are instead tackling the interoperability challenge by building a model driven/low-code Dig ital Thread platform that 1) systematizes the integration methodology, 2) provides methods and techniques for the individual integrations based on a layered Domain Specific Languages (DSL) approach, 3) through the DSLs it covers the integration space domain by domain, technology by technology, and is thus highly generalizable and reusable, 4) showcases a first collection of examples from the domains of robotics, IoT, data analytics, AI/ML and web applications, 5) brings cohesiveness to the aforementioned heterogeneous platform, and 6) is easier to understand and maintain, even by not specialized programmers. We showcase the power, versatility and the potential of the Digital Thread platform on four interoperability case studies: the generic extension to REST ser vices, to robotics through the UR family of robots, to the integration of various external databases (for data integration) and to the provision of data analytics capabilities in R

Efficient model-driven prototyping for edge analytics

Software development cycles in the context of Internet of Things (IoT) applications require the orchestration of different technological layers, and involve complex technical challenges. The engineering team needs to become experts in these technologies and time delays are inherent due to the cross-integration process because they face steep learning curves in several technologies, which leads to cost issues, and often to a resulting product that is prone to bugs. We propose a more straightforward approach to the construction of high-quality IoT applications by adopting model-driven technologies (DIME and Pyrus), that may be used jointly or in isolation. The presented use case connects various technologies: the application interacts through the EdgeX middleware platform with several sensors and data analytics pipelines. This web-based control application collects, processes and displays key information about the state of the edge data capture and computing that enables quick strategic decision-making. In the presented case study of a Stable Storage Facility (SSF), we use DIME to design the application for IoT connectivity and the edge aspects, MongoDB for storage and Pyrus to implement no-code data analytics in Python. We have integrated nine independent technologies in two distinct Low-code development environments with the production of seven processes and pipelines, and the definition of 25 SIBs in nine distinct DSLs. The presented case study is benchmarked with the platform to showcase the role of code generation and the reusability of components across applications. We demonstrate that the approach embraces a high level of reusability and facilitates domain engineers to create IoT applications in a low-code fashion</p