Enabling Trustful Data Sharing in Industry 4.0—A Comparison Between Data Spaces, Data Markets and Other Related Concepts

Springer eBooks, pp. 101–111

Abstract

Abstract Digitalization is an important part of Industry 4.0 in which data sharing is essential to exploit the full potential of data and enable the use of modern technologies like machine learning or predictive maintenance. In addition to trust, other boundary conditions, e.g., incentives, must be taken into account. This work investigates, compares and groups different approaches that can be used to increase data sharing. Designing a data market, which depends on the respective use case, appears to be a suitable high level solution for this. The paper points out that technological approaches to realize a trustful data market can be grouped in data spaces and data intermediaries as superordinated categories. Afterwards, different combinations in order to build a centralized, decentralized or hybrid trustful data market are shown. Finally, initial abstract design decision rules are derived.

Authors 2

1. Lennart Schinke corresponding Aachen Institute for Man-Machine Interaction

   RWTH Aachen University

   Affiliation as printed

   Institute for Man-Machine Interaction, RWTH Aachen University, Aachen, Germany

2. Jürgen Roßmann Aachen Institute for Man-Machine Interaction

   RWTH Aachen University

   Affiliation as printed

   Institute for Man-Machine Interaction, RWTH Aachen University, Aachen, Germany