Keywords: Digital Twin, Knowledge engineering, Model driven engineering, circular economy, sustainability
Astract: The next generation of engineered systems ought to be more sustainable. In this context, Digital Twins play a crucial role as key enablers of sustainability ambitions in systems engineering. However, as a specific class of engineered systems, Digital Twins themselves must adopt sustainability principles to avoid defeating their purpose in fostering sustainability. In this proposal, we focus on the technical sustainability of Digital Twins, enabled by their evolution. We propose an initial taxonomy we believe will support systematic Digital Twin evolution mecha-nisms and draw links to similar taxonomies of Physical Twins.

Keywords: Artificial intelligence, Conceptual modeling, Enterprise modeling, Machine learning, Model-driven engineering
Astract: Artificial Intelligence (AI) continuously paves its way into even the most traditional business domains. This particularly applies to data-driven AI, like machine learning (ML). Several data-driven approaches like CRISP-DM and KKD exist that help develop and engineer new ML-enhanced solutions. A new breed of approaches, often called canvas-driven or visual ideation approaches, extend the scope by a perspective on the business value an ML-enhanced solution shall enable. In this paper, we reflect on two recent ML projects. We show that the data-driven and canvas-driven approaches cover only some necessary information for developing and operating ML-enhanced solutions. Consequently, we propose to put ML into an enterprise context for which we sketch a first framework and spark the role enterprise modeling can play.

Keywords: ArchiMate, value co-creation
Astract: The design and engineering of collaborative networks and business ecosystems is a discipline that requires an outstanding and upfront attention of the value cogenerated among the parties involved in the business exchanges of these networks. Understanding this value co-creation is undoubtedly paramount, first to adequately sustain the design and the development of the information system that brings about this value, second, to support the communication between the information system designers, and third to allow discovering new co-creation opportunities among the networks companies. In that context, we proposed an abstract language (meta-model) that structures, and provides an explanatory semantics to, the co-creation of value between information system designers, allowing a better definition of the collaboration and of each one of the value propositions. The design of this language is achieved in the frame of the design science theory and accordingly follows an iterative improvement approach based on real case studies from practitioners. This chapter introduces the second iteration of the language based on a real case in a smart airport network.

Keywords: Conceptual Modeling
Astract: Fundamental decisions are made in the early phases of software development. The typical outcomes of these phases are models of different kinds, such as architectural models, data models, and process models. Automation support is required to efficiently and effectively handle large models and conduct continuous quality improvement processes. Thus, several approaches have been proposed that integrate modeling with Artificial Intelligence (AI) methods such as Genetic Algorithms (GAs), among others. These approaches, e.g., transform models to improve their quality by searching for good solutions within the potential solution space. In this chapter, we first review existing applications of AI methods to model-based software engineering problems. Subsequently, we show a representative use case of how a model-based software analysis and design problem can be solved using GAs. In particular, we focus on the well-known and challenging modularization problem: splitting an overarching, monolithic model into smaller modules. We present two encodings, the model-based and the transformation-based encoding, which are both applied for the modularization of Entity-Relationship (ER) diagrams. We further discuss how these encodings may be adapted to other structural models and conclude with an outlook on future research lines related to software modeling intelligence.

Keywords: Inclusive conceptual modeling, Inclusion, Diversity, Equity, Involvement, Belonging

Keywords: Digital Transformation
Astract: We posit that AI-Enabled Enterprise is the next phase of digital transformations seen so far. We will start by defining more precisely what we mean by digital transformation and argue that it’s important to ensure these transformations are well-coordinated. We posit this coordination should be seen as a continuous process and take a model-enabled perspective on it. In doing so, we will argue for the need to identify (and manage) so-called enterprise design dialogues, where enterprise models are positioned as a key artefact in support of these enterprise design dialogues. Before concluding, we also review some of the challenges and opportunities towards future research.

Keywords: Enterprise architecture, Historical analysis, Knowledge graph
Astract: Enterprise Architecture (EA) is the discipline that aims to provide a holistic view of the enterprise by explicating business and IT alignment from the perspectives of high-level corporate strategy down to daily operations and network infrastructures. EAs are consequently complex as they compose and integrate many aspects on different architecture layers. A recent proposal to cope with this complexity and to make EAs amenable to automated and intuitive visual analysis is the transformation of EA models into EA Knowledge Graphs. A remaining limitation of these approaches is that they perceive the EA to be static, i.e., they represent and analyze EAs at a single point in time. In the paper at hand, we introduce a historization concept, a prototypical implementation, and a performance analysis for how EAs can be represented and processed to enable the analysis of their evolution.

Keywords: DSL, EMF, Interoperability, Metamodeling, MSDKVS, Sirius
Astract: Many powerful metamodeling platforms exist, each with strengths, weaknesses, functionalities, programming language(s), and developer community. To exploit the mutual benefits of these platforms, it would be ideal to establish interoperability amongst them and the exchange of metamodels and models. This would enable language engineers to choose the metamodeling platform freely without risking a lock-in effect. Two well-documented and freely available metamodeling platforms are the Eclipse Modeling Framework (EMF) and Microsoft’s Modeling SDK for Visual Studio (MSDKVS). This paper proposes the first achievements toward establishing interoperability between EMF and MSDKVS on an abstract syntax level and a graphical concrete syntax level. To develop such interoperability, we i) comprehensively analyze the two platforms, ii) present a conceptual mapping between them, and iii) eventually implement a bidirectional transformation bridge. The transformed results’ validity, executability, and expressiveness are then quantitatively and qualitatively assessed by transforming a collection of publicly available metamodels.

Keywords: cyber-physical systems, digital twin, model-driven engineering, multi-paradigm modeling
Astract: Cyber-physical systems (CPS) are finding increasing use, whether in factories, autonomous vehicles, or smart buildings. Monitoring the execution of CPSs is crucial since CPSs directly influence their physical environment. Like the actual system, the monitoring application must be designed, developed, and tested. Mobile CPSs, in contrast to stationary CPSs, bring the additional requirement that instances can dynamically join, leave, or fail during execution time. This dynamic behavior must also be considered in the monitoring application. This paper presents CPSAML, a language and code generation framework for the model-driven development of mobile CPS systems, including a cockpit application for monitoring and interacting with such a system. The pipeline starts with the formulation of the system and the CPSs it contains at an abstract level by the system architect using a domain-specific modeling language. Next, this model is transformed into SysML 2 for further extension and richer specificity by system engineers on a more technical level. In the last step of the pipeline, the SysML 2 model is used to generate code for the CPS devices, a system-wide digital twin, and the cockpit application mentioned above. This cockpit enables the operator to configure and apply the monitoring and interaction with the system during runtime. We evaluate our CPSAML language and code generation framework on an Indoor Transport System case study with Roomba vacuum cleaner robots.

Keywords: Enterprise architecture, GEA method, Enterprise transformations
Astract: General Enterprise Architecting (GEA) is an enterprise architecture method which has emerged out of a need in practice, and has been developed and matured over the past 15 years. The GEA method differs from other enterprise architecture approaches in that it has a strong focus on enterprise coherence and the explicit governance thereof. This focus followed from the observed need to move beyond the Business-IT alignment and ‘Business-to-IT’ stack thinking that is embodied in most of the existing enterprise architecture approaches. The main objective of this paper is to report, and reflect on, the development of the GEA method (so-far), which involved a co-evolution between theory and practice. In doing so, we also present core elements of (the current version of) GEA, and illustrate these in terms of a real-world (social housing) case. We will, furthermore, also discuss some of the lessons learned in applying GEA across different organizations.