Abstract
In the production industry, the volume, variety and velocity of data as well as the number of deployed protocols increase exponentially due to the influences of IoT advances. While hundreds of isolated solutions exist to utilize this data, e.g., optimizing processes or monitoring machine conditions, the lack of a unified data handling and exchange mechanism hinders the implementation of approaches to improve the quality of decisions and processes in such an interconnected environment. The vision of an Internet of Production promises the establishment of a Worldwide Lab, where data from every process in the network can be utilized, even interorganizational and across domains. While numerous existing approaches consider interoperability from an interface and communication system perspective, fundamental questions of data and information interoperability remain insufficiently addressed. In this paper, we identify ten key issues, derived from three distinctive real-world use cases, that hinder large-scale data interoperability for industrial processes. Based on these issues we derive a set of five key requirements for future (IoT) data layers, building upon the FAIR data principles. We propose to address them by creating FactDAG, a conceptual data layer model for maintaining a provenance-based, directed acyclic graph of facts, inspired by successful distributed version-control and collaboration systems. Eventually, such a standardization should greatly shape the future of interoperability in an interconnected production industry.
Lars Gleim
Dr. rer. nat. Jan Pennekamp
Postdoctoral Researcher
Martin Liebenberg
Melanie Buchsbaum
Philipp Niemietz
Simon Knape
Alexander Epple
Simon Storms
Daniel Trauth
Thomas Bergs
Christian Brecher
Stefan Decker
Gerhard Lakemeyer
