| 1. |
- Barn, Balbir S., et al.
(författare)
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Adaptation of enterprise modeling methods for large language models
- 2024
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Ingår i: The practice of enterprise modeling. - Cham : Springer. - 9783031485824 - 9783031485831 ; , s. 3-18
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Konferensbidrag (refereegranskat)abstract
- Large language models (LLM) are considered by many researchers as promising technology for automating routine tasks. Results from applying LLM in engineering disciplines such as Enterprise Modeling also indicate potential for the support of modeling activities. LLMs are fine-tuned for specific tasks using chat based interaction through the use of prompts. This paper aims at a detailed investigation of the potential of LLMs in Enterprise Modeling (EM) by taking the perspective of EM method adaptation of selected parts of the modeling process within the context of using prompts to interrogate the LLM. The research question addressed is: What adaptations in EM methods have to be made to exploit the potential of prompt based interaction with LLMs? The main contributions are (1) a meta-model for prompt engineering that integrates the concepts of the modeling domain under consideration with the notation of the modeling language applied and the input and output of prompts, (2) an investigation into the general potential of LLM in EM methods and its application in the 4EM method, and (3) implications for enterprise modeling methods.
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| 2. |
- Griesch, L., et al.
(författare)
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Towards AI as a Service for Small and Medium-Sized Enterprises (SME)
- 2024
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Ingår i: The Practice of Enterprise Modeling. - : Springer. - 9783031485824 - 9783031485831 ; , s. 37-53
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Konferensbidrag (refereegranskat)abstract
- AI-as-a-Service (AIaaS) combines Artificial Intelligence (AI) and cloud computing to make AI accessible to enterprises without implementing complex solutions or technologies on-premise. Many small and medium-sized enterprises (SME) that lack competencies in the AI and technology sector consider AIaaS as a promising option to implement AI solutions. However, the differences between AIaaS and AI on-premise have not attracted much research. The intention of this paper is to contribute to this area by analysing the literature in the field and investigating a concrete example in more detail. Exploring AIaaS is crucial to better understand the opportunities and limitations of AI services. The contributions of the paper are (a) an analysis of the literature on AIaaS to identify factors affecting AI implementation and how AIaaS solutions differ from on-premise solutions when introducing AI in a company, (b) a case study of an SME that compares AIaaS and AI on-premise in practice, and (c) the application potential of a morphological box to compare AIaaS and AI on-premise.
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| 3. |
- Verhasselt, Yara, et al.
(författare)
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Evaluating ArchiMate for Modelling IoT Systems
- 2023
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Ingår i: The Practice of Enterprise Modeling. - : Springer. - 9783031485824 - 9783031485831 ; , s. 57-73
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Konferensbidrag (refereegranskat)abstract
- The Internet of Things (IoT) is a disruptive technology that allows connecting physical objects with the digital world. This challenges organizations in adjusting their Enterprise Architecture (EA) for adopting the IoT technology to improve their operations and maximize their value. Considering the complexity of such changes, a suitable modelling language must be used for EA design and documentation. ArchiMate, being a de facto standard for EA modelling, has already been used for modelling IoT systems, however, it is not used in a consistent manner and different extensions have been proposed to address limitations identified in specific domains. Based on the most common practices reported in the literature, we propose and evaluate a set of guidelines for modelling IoT elements in ArchiMate. The guidelines are evaluated by performing qualitative research based on case studies. The evaluation shows that ArchiMate is suitable for modelling IoT systems, but only at the conceptual level. When technical details are required, extensions to the standard may be needed. In addition, domain-specific limitations such as the lack of showing time-based communication and redundancy in an industry context are identified.
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| 4. |
- Zhemchugova, Helena, et al.
(författare)
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Using the Business Motivation Model as an Organising Principle to Clarify Public Policy
- 2023
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Ingår i: The Practice of Enterprise Modeling. - : Springer Nature. - 9783031485824 ; , s. 177-192
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Konferensbidrag (refereegranskat)abstract
- Public policies use constructive ambiguity as a linguistic and diplomatic tool to manage the conflicting views of stakeholders and facilitate policy implementation in the presence of tensions. However, the ambiguity of policy content is a knowledge problem. It can lead to misunderstandings of its ends and means and may result in failed mitigation measures for societal crises. The Business Motivation Model (BMM) combined with the UML attribute elements can solve this problem by structurally decomposing the policy content into a BMM representation. This representation is an epistemic artefact that generates meta-knowledge about the policy by clarifying its intentions and conceptual structure, thus decreasing the likelihood of it being misunderstood. We present the case of the Aviation Chapter of the Greenhouse Gas Emissions Trading System (EU ETS), where we clarify its prescriptions for the European Commission (EC), Member States, and aircraft operators using BMM concepts and relationships as deductive coding patterns. The artefact has undergone artificial empirical evaluation by expert opinion (n = 15). Possible use scenarios were discussed with enterprise modellers, climate policy researchers, and aircraft industry experts concerned with EU ETS enforcement, implementation, and auditing. The BMM representation presented advantages over the policy’s textual form and improved the aviation industry experts’ understanding of the Chapter. Future research could look into increasing the analytical capabilities of the notation to address the needs of policy researchers working with gap analyses and co-development of policy revisions with stakeholders. Other modelling and diagramming tools should be examined to enable model durability and robustness.
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