Inter-organizational data sharing holds significant potential for the industrial sector. It can enable process improvements and novel services and products that drive economic growth and sustainability. In this context, data itself becomes a product that can be shared on data marketplaces. However, the success of data marketplaces in the industrial sector has been poor. One reason is centralized marketplace architectures that limit the sovereignty of data providers and rely on the trustworthiness and neutrality of the marketplace operator. Federated architectures address this problem by giving autonomy and transparency to marketplace participants. In this paper, we report on the results of a two-year design science research project dealing with the conceptualization and implementation of a federated data marketplace in the industrial domain. Based on five design objectives we develop a conceptual architecture of a federated data marketplace, which we evaluate using a prototypical implementation.

E-commerce is one of the most important sectors of the economy, with an expected global turnover of USD 7.4 trillion in 2024. Leading companies in the industry like Amazon and Alibaba leverage platform business models that orchestrate complex ecosystems of buyers and sellers. These ecosystems are characterized by globally intertwined networks of independent participants and multifaceted interactions, making their development hard to predict. We propose an agent-based simulation approach, following the three-step process of Haki et al. (2020), to predict the behavior of e-commerce ecosystems with a particular focus on the impact of premium subscription services. Simulation models offer the opportunity to tangibly visualize the effects of a participant's actions on the ecosystem level. Our simulation includes the key actors (i.e., buyer, seller, marketplace), their characteristics, and behaviors to draw conclusions about the evolution of the ecosystem and the strategic role of premium memberships in shaping these digital landscapes.

Drift detection is essential in modern manufacturing, where it ensures the continuous achievement of high efficiency and quality. CV models efficiency is more likely to reduce overtime due to environmental changes, equipment wear and tear, or alterations in product specifications. Moreover, the collection of labeled data, essential for monitoring and adjusting these models, is often hindered by constraints. Addressing this gap, our research introduces an innovative unsupervised drift detection technique capable of identifying shifts in data distribution without the need for labeled data. This method proactively notifies operators of any decline in data quality that breaches a predetermined safety threshold, thereby preserving the operational integrity of CV applications. Our experimental results confirm the method's efficiency in detecting environmental shifts, changes in product specifications, and an increase in the production of defective parts, highlighting its significant potential to enhance quality control in manufacturing processes through reliable drift detection.