Experiences from the Internet-of-Production: Using “Data-Models-in-the-Middle” to Fight Complexity and Facilitate Reuse

Data-driven approaches play a key role in improving operational processes and production is no exception. The Internet-of-Production (IoP) is an ambitious initiative aiming at cross-domain collaboration in production while exploiting semantically adequate and context-aware data at different levels of granularity. The Internet-of-Things (IoT), in the context of production also referred to as Industry 4.0 or the Industrial Internet of Things, provides a wide range of data assets. However, these are often handled in an ad-hoc manner with little support for reuse. Data pipelines convert machine- or system-specific data into a format suitable for data-science techniques such as machine learning. Based on an analysis of the data used in IoP, we developed so-called “Data-Models-in-the-Middle” (DMMs). Two such models are described in this paper: Measurement and Event Data (MAED) and Object-Centric Event data (OCED). OCED enables Object-Centric Process Mining (OCPM), allowing organizations to view their operational processes from any perspective using a single source of truth. However, OCED is not suitable for low-level machine data that contain a mixture of continuous measurements (e.g., time series data describing position, temperature, force, speed, etc.) and discrete events. Therefore, we also propose MAED as a data format. The combination of both “Data-Models-in-the-Middle” (MAED and OCED) provides a good coverage of many production-related use cases.