Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical

Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical

Knowledge Extraction Using Probabilistic Reasoning: An Artificial Neural Network Approach

The World Wide Web (WWW) has radically changed the way in which we access, generate and disseminate information. Its presence is felt daily and with more internet-enabled devices being connected the web of knowledge is growing. We are now moving into era where the WWW is capable of ‘understanding’ the actual/intended meaning of our content. This is being achieved by creating links between distributed data sources using the Resource Description Framework (RDF). In order to find information in this web of interconnected sources, complex query languages are often employed, e.g. SPARQL. However, this approach is limited as exact query matches are often required. In order to overcome this challenge, this paper presents a probabilistic approach to searching RDF documents. The developed algorithm converts RDF data into a matrix of features and treats searching as a machine learning problem. Using a number of artificial neural network algorithms, a successfully developed prototype has been developed that demonstrates the applicability of the approach. The results illustrate that the Voted Perceptron classifier (VPC), perceptron linear classifier (PERLC) and random neural network classifier (RNNC) performed particularly well, with accuracies of 100%, 98% and 93% respectively

Designing Data Spaces

This open access book provides a comprehensive view on data ecosystems and platform economics from methodical and technological foundations up to reports from practical implementations and applications in various industries. To this end, the book is structured in four parts: Part I “Foundations and Contexts” provides a general overview about building, running, and governing data spaces and an introduction to the IDS and GAIA-X projects. Part II “Data Space Technologies” subsequently details various implementation aspects of IDS and GAIA-X, including eg data usage control, the usage of blockchain technologies, or semantic data integration and interoperability. Next, Part III describes various “Use Cases and Data Ecosystems” from various application areas such as agriculture, healthcare, industry, energy, and mobility. Part IV eventually offers an overview of several “Solutions and Applications”, eg including products and experiences from companies like Google, SAP, Huawei, T-Systems, Innopay and many more. Overall, the book provides professionals in industry with an encompassing overview of the technological and economic aspects of data spaces, based on the International Data Spaces and Gaia-X initiatives. It presents implementations and business cases and gives an outlook to future developments. In doing so, it aims at proliferating the vision of a social data market economy based on data spaces which embrace trust and data sovereignty

Is Your Organization Ready to Share? A Framework of Beneficial Conditions for Data Sharing

In a constantly evolving digital sphere, surmounting organizational boundaries and sharing data offers the opportunity to realize a multitude of mutual benefits, such as advanced analytics and innovative services. Organizations aspire to share data. However, they struggle to identify and establish beneficial conditions for data sharing, and research still offers little support to exploit the potential of data sharing. We apply an exploratory research approach to develop a framework of beneficial conditions for data sharing. By combining ten expert interviews and a systematic literature review, we aggregate 23 characteristics that constitute beneficial conditions into eight categories and apply and validate the framework in a real-world case. Thus, we contribute to research by providing a fundamental understanding of beneficial conditions for data sharing and a compact target picture. Additionally, we enable practitioners to systematically assess an organization’s current condition to set the course toward exploiting the full potential of data sharing

Linked Research on the Decentralised Web

This thesis is about research communication in the context of the Web. I analyse literature which reveals how researchers are making use of Web technologies for knowledge dissemination, as well as how individuals are disempowered by the centralisation of certain systems, such as academic publishing platforms and social media. I share my findings on the feasibility of a decentralised and interoperable information space where researchers can control their identifiers whilst fulfilling the core functions of scientific communication: registration, awareness, certification, and archiving. The contemporary research communication paradigm operates under a diverse set of sociotechnical constraints, which influence how units of research information and personal data are created and exchanged. Economic forces and non-interoperable system designs mean that researcher identifiers and research contributions are largely shaped and controlled by third-party entities; participation requires the use of proprietary systems. From a technical standpoint, this thesis takes a deep look at semantic structure of research artifacts, and how they can be stored, linked and shared in a way that is controlled by individual researchers, or delegated to trusted parties. Further, I find that the ecosystem was lacking a technical Web standard able to fulfill the awareness function of research communication. Thus, I contribute a new communication protocol, Linked Data Notifications (published as a W3C Recommendation) which enables decentralised notifications on the Web, and provide implementations pertinent to the academic publishing use case. So far we have seen decentralised notifications applied in research dissemination or collaboration scenarios, as well as for archival activities and scientific experiments. Another core contribution of this work is a Web standards-based implementation of a clientside tool, dokieli, for decentralised article publishing, annotations and social interactions. dokieli can be used to fulfill the scholarly functions of registration, awareness, certification, and archiving, all in a decentralised manner, returning control of research contributions and discourse to individual researchers. The overarching conclusion of the thesis is that Web technologies can be used to create a fully functioning ecosystem for research communication. Using the framework of Web architecture, and loosely coupling the four functions, an accessible and inclusive ecosystem can be realised whereby users are able to use and switch between interoperable applications without interfering with existing data. Technical solutions alone do not suffice of course, so this thesis also takes into account the need for a change in the traditional mode of thinking amongst scholars, and presents the Linked Research initiative as an ongoing effort toward researcher autonomy in a social system, and universal access to human- and machine-readable information. Outcomes of this outreach work so far include an increase in the number of individuals self-hosting their research artifacts, workshops publishing accessible proceedings on the Web, in-the-wild experiments with open and public peer-review, and semantic graphs of contributions to conference proceedings and journals (the Linked Open Research Cloud). Some of the future challenges include: addressing the social implications of decentralised Web publishing, as well as the design of ethically grounded interoperable mechanisms; cultivating privacy aware information spaces; personal or community-controlled on-demand archiving services; and further design of decentralised applications that are aware of the core functions of scientific communication

Supporting mobile mixed-reality experiences

Mobile mixed-reality experiences mix physical and digital spaces, enabling participants to simultaneously inhabit a shared environment online and on the streets. These experiences take the form of games, educational applications and new forms of performance and art, and engender new opportunities for interaction, collaboration and play. As mobile mixed-reality experiences move out of the laboratory and into more public settings they raise new challenges concerning how to support these experiences in the wild. This thesis argues that mobile mixed-reality experiences in which artists retain creative control over the content and operation of each experience, particularly those that are deployed as theatrical performances, require dedicated support for content authoring and reactive orchestration tools and paradigms in order to be successfully and robustly operated in public settings. These requirements are examined in detail, drawing on the experience of supporting four publicly toured mobile mixed-reality experiences; Can You See Me Now?, Uncle Roy All Around You, I Like Frank in Adelaide and Savannah, which have provided a platform to practically develop, refine and evaluate new solutions to answer these challenges in the face of presenting the experiences to many thousands of participants over a four year period. This thesis presents two significant supporting frameworks. The ColourMaps system enables designers to author location-based content by directly colouring over maps; providing a simple, familiar and yet highly flexible approach to matching location-triggers to complex physical spaces. It provides support for multiple and specialised content layers, and the ability to configure and manage other aspects of an experience, including filtering inaccurate position data and underpinning orchestration tools. Second, the Orchestration framework supports the day-to-day operation of public experiences; providing dedicated control-room tools for monitoring that reveal the content landscape and historical events, intervention and improvisation techniques for steering and shaping each participant's experience as it unfolds both physically and virtually, and processes to manage a constant flow of participants

Data Management Architecture for Serviceoriented Maritime Testbeds

In recent years, numerous new approaches that rely on data-intensive methods have been developed for maritime assistance systems, leading to a compelling need for more elaborate verification and validation procedures. Modern testbeds that can meet these demands are often developed separately from the system itself and provided as generically usable services. However, the joint usage of such testbeds by multiple stakeholders from research and industry confronts them with various challenges in terms of data management: Data control and protection is required to preserve possible competitive advantages or comply with legal framework conditions. The resulting decentralization in data management complicates collaboration, especially in the joint processing and analysis of testbed data. In this paper, we present a decentralized software system, which can deal with these challenges by modelling interrelationships between the stakeholders in a data space, considering their various interests. With the help of a modular data management architecture, the organization of a testbed data basis, as well as the support of verification and validation processes and the evaluation of data streams is made possible. This is achieved with a workflow model for mapping complex and distributed data processing steps. We demonstrate the applicability of the system in an application scenario for the development of a maritime assistance system