Towards Model-Driven Digital Twin Engineering: Current Opportunities and Future Challenges

Abstract

Digital Twins have emerged since the beginning of this millennium to better support the management of systems based on (real-time) data collected in different parts of the operating systems. Digital Twins have been successfully used in many application domains, and thus, are considered as an important aspect of Model-Based Systems Engineering (MBSE). However, their development, maintenance, and evolution still face major challenges, in particular: (i) the management of heterogeneous models from different disciplines, (ii) the bidirectional synchronization of digital twins and the actual systems, and (iii) the support for collaborative development throughout the complete life-cycle. In the last decades, the Model-Driven Engineering (MDE) community has investigated these challenges in the context of software systems. Now the question arises, which results may be applicable for digital twin engineering as well. In this paper, we identify various MDE techniques and technologies which may contribute to tackle the three mentioned digital twin challenges as well as outline a set of open MDE research challenges that need to be addressed in order to move towards a digital twin engineering discipline.

AIDOaRt: AI-augmented Automation for DevOps, a Model-based Framework for Continuous Development in Cyber-Physical Systems

“…Over the last two decades, MDE has become increasingly popular in software development projects. Indeed, it allows raising the level of abstraction and thus improving the ability to handle complex systems such as CPSs [1] The usage of models, as purposeful abstractions of systems and of their environment, is also of increasing importance for industrial applications, leading to new challenges e.g., digital twining [11]. The first generation MDE tools mostly focused on generating code from high-level platform-independent abstract descriptions.…”

DataTime: a Framework to smoothly Integrate Past, Present and Future into Models

“…Bordeleau et al [4] suggested that models at runtime are key for implementing digital twins. Our work feeds this claim in addition to adressing the following open issues, raised by their work: 1) Models and Data, and 2) Architectural Framework for Digital Twins for the specific case of spatio-temporal models.…”

“…The temporal one is made of the (huge) time series of data gathered from the many sensors available in the IPTS. The existing time series give the opportunity to not only provide a model at runtime [3], but also a full-fledged digital twin [4] of the IPTS to analyze, plan and manage the operations using machine learning techniques. However these space and time parts of the supporting IT are most often not well integrated, making it hard in practice for network operators to leverage the avalanche of data gathered from the IPTS.…”

Conceptualizing Digital Twins

“…A DT is a virtual representation (or replica) of an Actual System (AS) that is continuously updated with real-time data throughout its life-cycle and, at the same time, can interact with and influence the AS [2]. DTs can be built for a variety of purposes, such as for the design, development, analysis, simulation, and operations of non-digital systems in order to understand, monitor, and/or optimize the AS.…”