Smart devices and cyber-physical systems, which are interconnected to IT systems and services, form the basis for the arising Internet of Everything, opening up new economic opportunities for its participants and users beyond its technological aspects and challenges. While today's e-business scenarios are mostly dominated by a few centralized online platforms, future business models, which will be feasible for the Internet of Everything, need to address special requirements. Such business models, e.g., leveraging the possibilities of smart cities, need to cope with arbitrary combinations of products and services orchestrated into complex products in a highly distributed and dynamic environment. Furthermore, these arbitrary combinations are influenced by real-time context information derived from sensor networks or IT systems, as well as the users' requirements and preferences. The complexity of finding the optimal product/service combination overstrains users and leads to decisions according to the principle of adverse selection (i.e., choosing good enough instead of optimal). Such e-business models require an appropriate underlying value generation architecture that supports users in this process. In this paper, we develop a business model that addresses these problems. In addition, we present the Distributed Market Spaces (DMS) software-system architecture as a possible implementation, which enables the aforementioned decentralized and context-centric e-business scenario and leverages the commercial possibilities of smart cities.
One of the main topics within research activities is the management of research data. Large amounts of data acquired by heterogeneous scientific devices, sensor systems, measuring equipment, and experimental setups have to be processed and ideally be managed by Findable, Accessible, Interoperable, and Reusable (FAIR) data management approaches in order to preserve their intrinsic value to researchers throughout the entire data lifecycle. The symbiosis of heterogeneous measuring devices, FAIR principles, and digital twin technologies is considered to be ideally suited to realize the foundation of reliable, sustainable, and open research data management. This paper contributes a novel architectural approach for gathering and managing research data aligned with the FAIR principles. A reference implementation as well as a subsequent proof of concept is given, leveraging the utilization of digital twins to overcome common data management issues at equipment-intense research institutes. To facilitate implementation, a top-level knowledge graph has been developed to convey metadata from research devices along with the produced data. In addition, a reactive digital twin implementation of a specific measurement device was devised to facilitate reconfigurability and minimized design effort.
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