The importance of data is increasing along its inflation in our world today. In the big data era, data is becoming a main source for innovation, knowledge and insight, as well as a competitive and financial advantage in the race of information procurement. This interest in acquiring and exploiting data, in addition to the existing concerns regarding the privacy and security of information, raises the question of who should own the data and how the ownership of data can be preserved. This paper discusses and analyses the concept of data ownership and provides an overview on the subject from different point of views. It surveys also the state-of-the-art of data ownership in health, transportation, industry, energy and smart cities sectors and outlines lessons learned with an extended definition of data ownership that may pave the way for future research and work in this area.

The inclusion of usability heuristics into the metaverse is aimed at solving the unique issues raised by virtual reality (VR), augmented reality (AR), and mixed reality (MR) environments. This research points out the usability challenges of metaverse user interfaces (UIs), such as information overloading, complex navigation, and the need for intuitive control mechanisms in these immersive spaces. By adapting the existing usability models to suit the metaverse context, this study presents a detailed list of heuristics and sub-heuristics that are designed to improve the overall usability of metaverse UIs. These heuristics are essential when it comes to creating user-friendly, inclusive, and captivating virtual environments (VEs) that take care of the needs of three-dimensional interactions, social dynamics demands, and integration with digital–physical worlds. It should be noted that these heuristics have to keep up with new technological advancements, as well as changing expectations from users, hence ensuring a positive user experience (UX) within the metaverse.

Zusammenfassung: Machine Learning ist ein Forschungsfeld mit großen Potenzialen und weitreichenden Anwendungspotenzialen. Big Data kann dabei als Enabler angesehen werden, da große und qualitativ hochwertige Daten stets die Grundlage für erfolgreiche Machine Learning-Algorithmen und -Modelle darstellen. Aktuell gibt es noch keinen voll etablierten Standardprozess für den Machine Learning-Life Cycle, wie es im Data Mining mit dem CRISP-DM beispielsweise der Fall ist, was zur Folge hat, dass gerade die Operationalisierung von Machine Learning-Modellen Unternehmen vor große Herausforderungen stellen kann. In diesem Beitrag werden anhand der Sicht auf die Beschaffenheit der Daten, die verschiedenen Rollen in Machine Learning-Teams und den Lebenszyklus von Machine Learning-Modellen Implikationen für das Datenmanagement in Unternehmen herausgearbeitet.

Summary: How companies report their data is undergoing digitization and sustainable transformation. Sustainability is important; therefore, various stakeholders are interested in sustainability information. Companies provide the required information and strive toward the use of information systems to ensure efficient data processing. A possible approach for information provision is open data. This research introduces the idea of corporate sustainability open data (CSOD) as one new mechanism of companies' sustainability self‐reporting. Since CSOD is not yet commonly practiced by companies, a strategic analysis of the situation and its possible consequences is conducted with an analysis of strengths, weaknesses, opportunities, and threats. This research provides an overview of companies' sustainable development through open data. Moreover, it identifies drivers, challenges, and reasonable strategies for CSOD adoption. Thus, the research contributes to the establishment of an innovative application of open data in the private sector to support sustainable transformation worldwide.

Artificial Intelligence (AI) technologies are promised to improve digital services and automate tasks. However, there are still significant barriers to ensuring that AI technologies are accessible and usable by a broad range of users. As AI solutions proliferate across mainstream systems and applications, design-based approaches that explicitly bring in inclusive and human-centric values have become critical. This paper provides a concerted look at user-centered design at the intersection of AI, accessibility, and usability, proposing a framework that cuts across technological, social, and regulatory challenges. Contributions include identifying existing work and current literature gaps, key research questions, and a methodology to explore how to optimize AI systems for the widest possible range of users. We anchor our recommendations with a use-inspired case of an AI-driven public transportation assistant for individuals with diverse physical and cognitive abilities to demonstrate how our framework could benefit real-world applications. On the basis of existing standards and theoretical insights, this paper argues that the design process should be proactive, iterative, and implemented with the participation of multiple stakeholders. In their design of AI systems, this is meant to make the systems adaptive to users, rather than users being adaptive to the AI systems, thus revealing that “AI for all” can indeed be a realistic and realizable paradigm.