The EDI hive IoT framework is a cloud system based on the latest microservices architecture, which allows for a flexible orchestration of resources. This means that many user groups with large amounts of **data** can access the platform for different applications without performance restrictions. For **data** safety purposes, the platform can be operated as a private cloud on-or off-premises [9]. In addition to general use, role/rights, measurement **data** from management functions (e.g., aggregated measurement data), etc., the EDI hive offers other generic applications that can be used to optimize and control processes ( Figure 3). In particular, the "EDI hive Cause-and-Effect Chain Editor" can be employed to formalize existing expert knowledge and use it for the calibration of AI-based models [10]. With the EDI hive standard names application, the system and system parameter names in the EDI hive can be semantically networked with regard to different languages. Furthermore, specific and frequently abbreviated control and measurement system channel names can be translated into different languages. In contrast, relevant status **data** can be calculated or predicted from simple measurable parameters using valid simulation models or based on expert knowledge. The acquisition of such parameters can be referred to as a soft sensor supporting the AI algorithms, which can then be trained with a larger and technically more meaningful database and be calibrated for prediction [6,7].