## 4 Problem characterization and Abstraction
### Challenges in Data Acquisition
#### 4.1.1 Analysis of Existing System Architectures  
The integration and scalability of data acquisition systems in industrial environments present multiple challenges, both technically and organizationally. One of the main difficulties lies in integrated data networking, as data are often redundant and dispersed across multiple systems in varying formats, making its processing complex. The autonomy of data source systems results in independent data structures, interfaces, exchange formats, and communication protocols, contributing to data heterogeneity [5].  
A major challenge in industrial data acquisition is the integration of heterogeneous data sources, as they often use different formats and protocols. To address this issue, we propose a modular system architecture that facilitates seamless data acquisition, transformation, and analysis [74]. The proposed architecture is depicted in Figure 5.  
Figure 5: System design by [PERSON] (figure by [PERSON]).  
An effective data management strategy is essential to ensuring data quality and preventing multi-source conflicts, such as contradictory values or inconsistent units. To achieve this, data should be stored in a lean backbone using neutral, transparent, and archivable formats [5]. Additionally, modularization and reusability of analytical processes are necessary to facilitate scalability and knowledge transfer across projects. A well-documented and flexible module-based system allows for selecting and customizing components to meet specific analytical requirements [5].  
The scalability of existing system architectures is another challenge, as they often fail to keep pace with the rapidly increasing data volumes and industrial requirements. A modular and adaptable approach to data analysis is needed to ensure long-term usability and efficient processing [5]. Furthermore, the heterogeneous nature of industrial use cases requires analytical modules to be highly customizable and adaptable to different data sources, which poses a significant challenge in maintaining interoperability across systems [5].  
Efficiency is another critical factor, as systems must seamlessly integrate practical data analysis with continuous competency development and learning processes. This includes incorporating advanced analytical techniques such as supervised, semi-supervised, and unsupervised learning to optimize decision-making and predictive capabilities [5].  
To address latency issues, edge computing has emerged as a powerful solution, allowing real-time data processing at the network edge. This significantly reduces response times, which is particularly important in industrial automation applications requiring instantaneous reactions [75]. Additionally, decentralized data processing via edge computing enhances security, as data are processed and stored locally, reducing the risk of data privacy violations [76].  
Another critical aspect of modern system architectures is human-machine interaction (HMI). Industry 4.0 has transformed industrial process control and monitoring by enhancing the interconnectivity of machines and systems. As a result, intuitive HMI design is essential for improving operator efficiency and reducing error rates. [PERSON] ref. [77] emphasize that improved HMI design enhances productivity by facilitating smoother communication between operators and machines. Effective interface design fosters greater user acceptance and reduces operational friction. Moreover, the study highlights the importance of adaptive systems and feedback mechanisms, which enable interfaces to dynamically adjust based on user capabilities and system conditions, ensuring optimal interaction efficiency [77].  
These challenges highlight the critical role of data acquisition and processing in industrial environments and must be carefully considered in the design of a robust and scalable system architecture.