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PublicationSetting the course: Gaia-X and the future of datacentric government( 2024)The digitization of local government has seen a growing trend towards data networking. Over the past 20 years, “smart cities” and “smart regions” have emerged as a key concept in this trend. Smart cities and smart regions are areas of settlement where highly integrated networking enables the regular use of (ecologically, economically, and socially) sustainable products, services, technologies, processes, and infrastructures. Of enormous importance are the next steps towards a networked, data-centric administration of cities and regions. On the one hand, the use of data-based systems will expand and improve public services and sovereignty over tasks. On the other hand, more and more public and private actors will be involved along the (digital) value chains. Furthermore, the growing need for sustainable and resilient administrations underscores the importance of developing digital tools and solutions for urban and regional development. Finally, these digital solutions will accelerate the transformation of the energy, transport, and infrastructure sectors. This document aims to describe the concepts of data-centric government and the organizational and technical requirements necessary to achieve it. Section 1 provides a general introduction to data-centric government, addressing governance challenges and legal frameworks. Section 2 introduces the Gaia-X initiative, first in general terms and then as a tool to combine data sets and connect data services and facilitate the digital transformation of local government. Section 3 describes the technical aspects of digital transformation in government: from the current use of data to the technological and procedural tools available. The document concludes with four use cases illustrating the benefits of digitization and data orientation for governments.
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PublicationDeutsche Normungsroadmap Künstliche Intelligenz(DIN, 2022-12-09)
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PublicationGenerative Machine Learning for Resource-Aware 5G and IoT SystemsExtrapolations predict that the sheer number of Internet-of-Things (IoT) devices will exceed 40 billion in the next five years. Hand-crafting specialized energy models and monitoring sub-systems for each type of device is error prone, costly, and sometimes infeasible. In order to detect abnormal or faulty behavior as well as inefficient resource usage autonomously, it is of tremendous importance to endow upcoming IoT and 5G devices with sufficient intelligence to deduce an energy model from their own resource usage data. Such models can in-turn be applied to predict upcoming resource consumption and to detect system behavior that deviates from normal states. To this end, we investigate a special class of undirected probabilistic graphical model, the so-called integer Markov random fields (IntMRF). On the one hand, this model learns a full generative probability distribution over all possible states of the system-allowing us to predict system states and to measure the probability of observed states. On the other hand, IntMRFs are themselves designed to consume as less resources as possible-e.g., faithful modelling of systems with an exponentially large number of states, by using only 8-bit unsigned integer arithmetic and less than 16KB memory. We explain how IntMRFs can be applied to model the resource consumption and the system behavior of an IoT device and a 5G core network component, both under various workloads. Our results suggest, that the machine learning model can represent important characteristics of our two test systems and deliver reasonable predictions of the power consumption.
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PublicationIDS Reference Architecture Model. Industrial Data Space. Version 2.0(International Data Spaces Association, 2018)
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PublicationReference Architecture Model for the Industrial Data Space(Fraunhofer-Gesellschaft, 2017)
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