Large Language Models (LLMs) have the potential to support the transformation of natural language legal text into a regulatory model, a task conventionally known to be time consuming and error prone when done manually. In this paper, we introduce CLERK: a C ompanion L LM E xpert for modeling R egulatory K nowledge existing in natural language legal texts. CLERK captures regulatory knowledge in the format of Legal Goal Requirements Language (GRL) models. CLERK offers three key contributions, utilizing established prompting techniques: (1) Adopting the Tree-of-Thought (ToT) prompting framework, CLERK streamlines the regulatory modeling process by breaking down complex steps into manageable tasks and focusing on those essential for constructing a Legal GRL model only. (2) The ToT framework enables self-evaluation of intermediate outputs. (3) CLERK enhances consistency and clarity, by leveraging additional in-context learning prompting techniques, such as few-shot prompting and output formatting with an explicit syntax definition. Experiments with eight regulatory articles from two domains (healthcare and energy communities) display a notable improvement brought about by CLERK compared to previous approaches. This improvement pertains to identifying relevant actors, goals and their deontic modalities, as well as the relationships among goals.

To support the value assessment of technically feasible smart grid initiatives there exist several valuation methods. To determine whether those methods address all concerns relevant for smart grid valuation, we carry out a literature analysis aiming at (1) identifying existing valuation methods and the steps they propose, (2) identifying important valuation considerations, and (3) confronting these considerations with artifacts proposed by the existing valuation methods to identify open issues, requirements, and remaining challenges. Based on the conducted analysis we identify, among others, the following main deficiencies: (1) only a limited scope of concerns relevant to valuation is covered, particularly a systematic consideration of stakeholders goals, value exchange scenarios, and the IT infrastructure is lacking; and (2) a lack of instruments dedicated to fostering accessibility of valuation, in terms of establishing a shared understanding, communicating results, or actively involving different stakeholders in the process. Based on the findings, we suggest the application of conceptual modeling as an instrument to address the identified deficiencies. Therefore, we reflect on the role that current modeling approaches can play in smart grid valuation. This paper is a part of a larger project whose ultimate goal is to develop a model-based method for multi-perspective valuation of smart grid initiatives. The purpose of this paper is to establish a foundation for the realization of the envisioned method. The design of the model-based valuation method itself, its application and evaluation, are subjects of future work.

Our work aims to rationalize Enterprise Architectures (EA) by providing the reasoning behind the designs, in terms of selection criteria, design alternatives and more. Its major contribution is a formal metamodel that captures the reasoning and the inter-relationships of design decisions. This paper extends our approach in order to provide an explicit bridging between the Problem space that is defined by the different requirements and the Solution space that is described by specific design decisions. In doing so, EA Anamnesis also supports traceability from specific design decisions to the given requirements.

We aim for rationalizing Enterprise Architecture (EA) languages, showing not only final EA designs, but also the reasoning behind these designs (in terms of selection criteria, design alternatives, and more).

Enterprise Architecture (EA) modeling languages, such as ArchiMate, describe an enterprise holistically. In doing so, they show an enterprise's business products and services, and how these are realized by IT infrastructure and applications. However, EA modeling languages lack the capability to capture design rationales for decisions that lead to specific architectural designs. In our previous work we presented the EA Anamnesis approach for capturing decision details behind EA models. In doing so, we focused on capturing individual architectural decisions (in terms of alternatives, decision criteria, et cetera). In this paper we present an approach for relating architectural decisions. Using decision design graphs, we make explicit how decisions from different enterprise domains (Business, Application, Technology) relate to each other. For example, how decisions taken on a business level affect IT decisions and vice versa. Our approach is inspired by well-known mechanisms for capturing architectural rationales in software architecture. Specifically we contribute: (1) a decision relationship metamodel for enterprise architecture, with a focus on recording the impact of decisions (2) the notion of a Decision Design Graph for enterprise architecture, a visual representation of this metamodel, and (3) an illustrative example illustrating the potential usefulness of capturing decision relationships.

ArchiMate is a Domain Specific Language (DSL) to model an enterprise from a holistic perspective, showing not only the IT infrastructure of an organization, but also how this IT infrastructure supports business processes and contributes to the realization of products and (commercial) services.

Electronic service bundles such as Internet access and voice over IP become increasingly important for the economy. The requirements engineering problem is how to compose an e-service bundle such that consumer needs are met optimally, and the suppliers provide the services economically sustainable. We propose a technique to match a consumer need with a multi-supplier bundle of commercial e-services, which we illustrate by means of a telecommunication case study.