The use of Decentralized and Semantic Web Technologies for Personal Data Protection and Interoperability

The enactment of the General Data Protection Regulation (GDPR) has been the response of the European Union to the growing data-driven economy backed up by the largest companies in the world. It provides the data protection and portability needed by individuals that \u201cunconsciously\u201d generate personal data for \u201cfree\u201d services offered by providers that lack transparency on their use. Meanwhile, the rise of Distributed Ledger Technologies (DLTs) offers new possibilities for the management of general purpose data, hence being suitable for handling personal data in a trustless scenario. These decentralized technologies bring a new concept of contract called smart because of its ability to be self-executable. DLTs and smart contracts, together with the use of Semantic Web standards, allows the creation of a decentralized digital space controlled entirely by an individual, where his personal data can be stored and transacted

Trust, Accountability, and Autonomy in Knowledge Graph-based AI for Self-determination

Knowledge Graphs (KGs) have emerged as fundamental platforms for powering intelligent decision-making and a wide range of Artificial Intelligence (AI) services across major corporations such as Google, Walmart, and AirBnb. KGs complement Machine Learning (ML) algorithms by providing data context and semantics, thereby enabling further inference and question-answering capabilities. The integration of KGs with neuronal learning (e.g., Large Language Models (LLMs)) is currently a topic of active research, commonly named neuro-symbolic AI. Despite the numerous benefits that can be accomplished with KG-based AI, its growing ubiquity within online services may result in the loss of self-determination for citizens as a fundamental societal issue. The more we rely on these technologies, which are often centralised, the less citizens will be able to determine their own destinies. To counter this threat, AI regulation, such as the European Union (EU) AI Act, is being proposed in certain regions. The regulation sets what technologists need to do, leading to questions concerning: How can the output of AI systems be trusted? What is needed to ensure that the data fuelling and the inner workings of these artefacts are transparent? How can AI be made accountable for its decision-making? This paper conceptualises the foundational topics and research pillars to support KG-based AI for self-determination. Drawing upon this conceptual framework, challenges and opportunities for citizen self-determination are illustrated and analysed in a real-world scenario. As a result, we propose a research agenda aimed at accomplishing the recommended objectives

Enabling interoperable distributed ledger technology with legacy platforms for enterprise digitalization

Presently to achieve enterprise digitalization technologies such as Distributed Ledger Technologies (DLT) has now been deployed to support digital services provided by enterprises. But several challenges in DLTs remain to be addressed, including the interoperability, standardization, and integration. Therefore, this study provides theoretical and practical understanding of DLT interoperability and identified the factors that influence the interoperability of DLTs. Also, an architecture is designed to shows how interoperability can be achieved in DLTs and legacy systems supported by Application Programming Interface (API). A case study is presented to illustrate the applicability of the architecture to support a digital energy marketplace.acceptedVersio

Internet of Things: A Review of Surveys Based on Context Aware Intelligent Services

The Internet of Things (IoT) has made it possible for devices around the world to acquire information and store it, in order to be able to use it at a later stage. However, this potential opportunity is often not exploited because of the excessively big interval between the data collection and the capability to process and analyse it. In this paper, we review the current IoT technologies, approaches and models in order to discover what challenges need to be met to make more sense of data. The main goal of this paper is to review the surveys related to IoT in order to provide well integrated and context aware intelligent services for IoT. Moreover, we present a state-of-the-art of IoT from the context aware perspective that allows the integration of IoT and social networks in the emerging Social Internet of Things (SIoT) term.This work has been partially funded by the Spanish Ministry of Economy and Competitiveness (MINECO/FEDER) under the granted Project SEQUOIA-UA (Management requirements and methodology for Big Data analytics) TIN2015-63502-C3-3-R, by the University of Alicante, within the program of support for research, under project GRE14-10, and by the Conselleria de Educación, Investigación, Cultura y Deporte, Comunidad Valenciana, Spain, within the program of support for research, under project GV/2016/087. This work has also been partially funded by projects from the Spanish Ministry of Education and Competitivity TIN2015-65100-R and DIIM2.0 (PROMETEOII/2014/001)

Managing Customer Data in Data-driven Service Innovation: A Framework of Data Principles

While customer data has been collected in enterprise systems since decades, the emerging consumer technologies create new sources of data. Although the need to co-create services with customers has been recognized, a systematic approach of how to include this sensitive source of innovation in the service innovation process is still lacking. This research explores the potential of data governance practices for data-driven service innovation. Data principles for the governance of customer data are collected and assessed by practioners in order to provide conceptual support for organizations and to facilitate the service innovation process. The results of this research integrate data principles of different research streams and offer a framework of data principles that can be applied in the design and management of data-driven services

Trust, Accountability, and Autonomy in Knowledge Graph-Based AI for Self-Determination

Knowledge Graphs (KGs) have emerged as fundamental platforms for powering intelligent decision-making and a wide range of Artificial Intelligence (AI) services across major corporations such as Google, Walmart, and AirBnb. KGs complement Machine Learning (ML) algorithms by providing data context and semantics, thereby enabling further inference and question-answering capabilities. The integration of KGs with neuronal learning (e.g., Large Language Models (LLMs)) is currently a topic of active research, commonly named neuro-symbolic AI. Despite the numerous benefits that can be accomplished with KG-based AI, its growing ubiquity within online services may result in the loss of self-determination for citizens as a fundamental societal issue. The more we rely on these technologies, which are often centralised, the less citizens will be able to determine their own destinies. To counter this threat, AI regulation, such as the European Union (EU) AI Act, is being proposed in certain regions. The regulation sets what technologists need to do, leading to questions concerning How the output of AI systems can be trusted? What is needed to ensure that the data fuelling and the inner workings of these artefacts are transparent? How can AI be made accountable for its decision-making? This paper conceptualises the foundational topics and research pillars to support KG-based AI for self-determination. Drawing upon this conceptual framework, challenges and opportunities for citizen self-determination are illustrated and analysed in a real-world scenario. As a result, we propose a research agenda aimed at accomplishing the recommended objectives

Internet of Things Strategic Research Roadmap

Internet of Things (IoT) is an integrated part of Future Internet including existing and evolving Internet and network developments and could be conceptually defined as a dynamic global network infrastructure with self configuring capabilities based on standard and interoperable communication protocols where physical and virtual “things” have identities, physical attributes, and virtual personalities, use intelligent interfaces, and are seamlessly integrated into the information network

Designing semantic Application Programming Interfaces for open government data

Many countries currently maintain a national data catalog, which provides access to the available datasets – sometimes via an Application Programming Interface (API). These APIs play a crucial role in realizing the benefits of open data as they are the means by which data is discovered and accessed by applications that make use of it. This article proposes semantic APIs as a way of improving access to open data. A semantic API helps to retrieve datasets according to their type (e.g., sensor, climate, finance), and facilitates reasoning about and learning from data. The article examines categories of open datasets from 40 European open data catalogs to gather some insights into types of datasets which should be considered while building semantic APIs for open government data. The results show that the probability of inter-country agreement between open data catalogs is less than 30 percent, and that few categories stand out as candidates for a transnational semantic API. They stress the need for coordination - at the local, regional, and national level - between data providers of Germany, France, Spain, and the United Kingdom.The authors gratefully acknowledge funding from the European Union through the GEO-C project (H2020-MSCA-ITN-2014, Grant Agreement Number 642332, http://www.geoc. eu/). Carlos Granell has been funded by the Ramón y Cajal Programme (grant number RYC- 2014-16913). Sergio Trilles has been funded by the postdoctoral programme Vali+d (GVA) (grant number APOSTD/2016/058)

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