Efficient data exploration is crucial as data becomes increasingly important for accelerating processes, improving forecasts and developing new business models. Data consumers often spend 25-98 % of their time searching for suitable data due to the exponential growth, heterogeneity and distribution of data. Data catalogs can support and accelerate data exploration by using metadata to answer user queries. However, as metadata creation and maintenance is often a manual process, it is time-consuming and requires expertise. This study investigates whether LLMs can automate metadata maintenance of text-based data and generate high-quality DCAT-compatible metadata. We tested zero-shot and few-shot prompting strategies with LLMs from different vendors for generating metadata such as titles and keywords, along with a fine-tuned model for classification. Our results show that LLMs can generate metadata comparable to human-created content, particularly on tasks that require advanced semantic understanding. Larger models outperformed smaller ones, and fine-tuning significantly improves classification accuracy, while few-shot prompting yields better results in most cases. Although LLMs offer a faster and reliable way to create metadata, a successful application requires careful consideration of task-specific criteria and domain context.

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Data is the main driver of the digital economy. Accordingly, companies are interested in maintaining technical control over the usage of their data at any given time. The International Data Spaces initiative addresses exactly this aspect of data sovereignty with usage control enforcement. In this paper, we introduce the so-called Workflow with Data and Usage control network (WFDU-net) model. The data consumer can visually define his or her workflow using the WFDU-net model and annotate the data operations and context. With model checking we validate that the WFDU-net follows the usage policies defined by the data owner. Afterwards, the compliant WFDU-net can be executed by exporting the WFDU-net in a Petri Net Markup Language (PNML). We evaluated our approach by using our example WFDU-net in a data analytics use case.

Data forms an essential organizational asset and is a potential source for competitive advantages. To exploit these advantages, the engineering of data-intensive applications is becoming increasingly important. Yet, the professional development of such applications is still in its infancy and a practical engineering approach is necessary to reach the next maturity level. Therefore, resources and frameworks that bridge the gaps between theory and practice are required. In this study, we developed a data engineering reference model (DERM), which outlines the important building-blocks for handling data along the data lifecycle. For the creation of the model, we conducted a systematic literature review on data lifecycles to find commonalities between these models and derive an abstract meta-model. We successfully validated our model by matching it with established data engineering topics. Using the model derived six research gaps that need further attention for establishing a practically-grounded engineering process. Our model will furthermore contribute to a more profound development process within organizations and create a common ground for communication.

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