## 1 Introduction  
In the data-driven economy of Industry 4.0, the ability to efficiently collect, analyze, and utilize data is crucial for competitiveness and innovation [1, 2]. Modern knowledge management approaches incorporate human-machine interaction and data protection, playing a key role in industrial applications [3]. The transition to a Knowledge Society 4.0 underscores the importance of data-driven value creation, with seven core knowledge management tasks: data collection, processing, knowledge generation, distribution, utilization, fostering learning organizations, and adapting to technological advancements [4]. To better understand this evolution, Figure 1 illustrates the four stages of industrial transformation--from mechanization to digitalization. While not central to the technical contributions of this work, it serves as a contextual reference point to position the architectural challenges of Industry 4.0 within the broader historical landscape. Efficient industrial data analysis requires integrated systems, such as the reference framework developed in the AKKORD research project [5]. However, the growing heterogeneity of data sources and the demand for real-time processing pose significant challenges. Addressing these issues requires a well-defined system architecture, enabling interdisciplinary collaboration, scalable integration, and adaptive knowledge modeling [6]. The fourth industrial revolution (Industry 4.0) has transformed traditional manufacturing by introducing cyber-physical systems, connectivity, and autonomous decision-making [7; 8; 9]. As industrial systems increasingly rely on data-driven control and predictive analytics, the ability to continuously acquire, process, and contextualize data from heterogeneous sources becomes a core challenge [10]. This includes integrating legacy devices [11], new smart sensors, and distributed control systems--each with different protocols, semantics, and update cycles. However, existing architectural frameworks often lack the flexibility, semantic interoperability, and real-time processing capabilities required to unify such diverse industrial environments [12]. Most reference models focus either on high-level integration (e.g., RAMI 4.0) or low-level communication standards (e.g., OPC UA), but fail to provide a scalable, modular approach that supports continuous knowledge modeling alongside dynamic data acquisition. This study addresses this critical gap by proposing a modular architecture that combines native protocol integration, semantic abstraction, and edge-cloud processing. It is evaluated in a real-world industrial environment, focusing on the system's ability to collect, transform, and utilize live data for knowledge-based applications. Industry 4.0 transformation is revolutionizing production processes through IoT and cyberphysical systems (CPS), facilitating automation, data exchange, and decentralized control structures [7; 8; 9]. This shift improves efficiency, process transparency, and value chain integration, creating new opportunities for data-driven decision-making [13]. However, Industry 4.0 is not only a technological evolution--it requires fundamental changes in management structures and a shift toward intelligent, self-optimizing production systems [14]. As shown in Figure 1, the evolution from mechanical power to interconnected digital systems highlights the complexity of modern industrial requirements. To address these emerging challenges, this research develops a system architecture for dynamic data acquisition and knowledge modeling in industrial environments. The aim is to create a scalable and flexible framework that enables the seamless integration of heterogeneous data sources while ensuring real-time analytics and interoperability. By  
Figure 1: Stages of the industrial revolution, illustrating the historical development up to Industry 4.0 (figure by [PERSON]).