Datenschutz

Load carriers are essential for transporting manufactured parts in manufacturing companies. Despite their ‘simplicity’, they are usually expensive to purchase as they are manufactured expressly to fit purpose. While tracking methods such as GPS tracking can be used to prevent the loss of load carriers, this is associated with monitoring costs and presents challenges with regard to data protection as soon as the work performance of intralogistics employees is monitored. Assigning load carriers to designated clusters and monitoring these clusters provides an effective solution—without drawing conclusions about employee performance. Furthermore, artificial intelligence can optimize this approach whilst also deterring the theft of load carriers.

The decentralized data ecosystem Gaia-X, which is currently under development, supports the future viability of the digital data economy in Europe. But how can relevant use cases be realized in Gaia-X from a service-oriented perspective? To answer this question, this article presents a methodology that describes a structured and interdisciplinary approach to service development in the ongoing Gaia-X 4 ROMS consortium research project [1]. In this project, federated services are realized in five processing steps on the basis of use cases. IT experts, software developers and industry users can leverage the model to efficiently coordinate the joint realization of use cases with Gaia-X and the goal of sovereign data exchange.

Artificial Intelligence (AI) can make a major contribution to the future viability of our economy and society—whether by improving existing processes or new products and services that promise greater efficiency, more robust structures and more climate protection. At present, however, SMEs in particular are still reluctant to use AI systems. The frequently cited reason is that data protection hurdles appear to be too high. This article discusses the opportunities of data-based value creation. The central question is how AI applications in industry can generate economic added value from data while maintaining data protection and security.

With the advent of People Analytics Big Data are made operative on the level of personnel management. An algorithm-based screening and analysis of all kinds of employee-related data and behavioral traces is expected to guarantee evidence-based decisions in an organizational field once considered ‘soft’ and subjective. The article summarizes the objectives and challenges of such data-driven personnel management, as well as its implications for employees and corporate labour policies.

Blockchain and distributed ledger technologies (BC/DLT) have attracted social and scientific attention at least since the success of Bitcoin and Ethereum as so-called cryptocurrencies. This attention leads to multidisciplinary dynamics whose euphoria often leads to a neglect of scientific thoroughness. As one facet, immutability is a core characteristic and often postulated property of the system. However, it is hardly possible to identify an analysis of the counter side, namely the sustainability of this technology. This article deals with different aspects of the sustainability of BC/DLT and relates them to the requirements of BC/DLT.

Machine learning is the key technology of almost every instance of modern Artificial Intelligence. Enormous datasets are produced in digitized industrial processes and in the Internet of Things, which can well be exploited by learning in deep artificial neural networks. Standard machine learning algorithms require these datasets to be centralized before learning a model. Several good reasons - ranging from data privacy over latency to economic efficiency - favor learning at the edge so that reasoning is fast and no local data is transferred. The article shows how decentralized learning works and how to evaluate it. Moreover, we point to special resource-efficient learning algorithms and discuss small remaining risks of data reconstruction.

What do new technologies and the increasing cyber threat hold in store for business and production in 2016? How should organizations be preparing themselves? What should IT security leaders be doing as a priority in the coming year? These are the questions of leading security analysts and consultants at TÜV Rheinland to tackle. The year 2016 will see an increasing number of attacks and the emergence of new targets. The complexity and sophistication of attacks, initiated by increasingly capable and technically well-equipped cyber criminals, will continue to rise. Industrial Control System Security (ICS) and Incident Response is more important than ever.