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FactDAG: Formalizing Data Interoperability in an Internet of Production

: Gleim, L.; Pennekamp, J.; Liebenberg, M.; Buchsbaum, M.; Niemietz, P.; Knape, S.; Epple, A.; Storms, S.; Trauth, D.; Bergs, T.; Brecher, C.; Decker, S.; Lakemeyer, G.; Wehrle, K.


IEEE internet of things journal : IITJAU 7 (2020), No.4, pp.3243-3253
ISSN: 2327-4662
Journal Article
Fraunhofer IPT ()
Fraunhofer FIT ()
Fraunhofer FKIE ()

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 the Internet-of-Things (IoT) advances. While hundreds of isolated solutions exist to utilize these 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 article, 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.