Data Streaming Platform for Crowd-Sourced Vehicle Dataset Generation

The European Commission has a program to accelerate the Digital Transition and is putting forward a vision based on cloud, common European Data Spaces and AI. As the data space paradigm unfolds across Europe, the Green Deal Data Space emerges. Its foundational pillars are to be built by the GREAT project. Data Spaces will be built over federated data infrastructures with common technical requirements (where possible) taking into account existing data sharing initiatives. Services and middleware developed to enable a federation of cloud-to-edge capacities will be at the disposal of all data spaces.GREAT, the Green Deal Data Space Foundation and its Community of Practice, has the ambitious goal of defining how data with the potential to help combat climate and environmental related challenges, in line with the European Green Deal, can be shared more broadly among many stakeholders, sectors and boundaries, according to European values such as fair access, privacy and security. The project will consider and incorporate community defined requirements and use case analyses to ensure that the resulting data space infrastructure is designed and built with and for the users. An implementation roadmap will guide the efforts of multiple actors to converge toward a blueprint technical architecture, a data governance scheme that enables innovative business cases, and an inventory of high value datasets, that will enable proof of concept, implementation and scale-up of a minimum viable green deal data space.This roadmap will identify the resources and other key ingredients needed for the Green Deal Data Space to be successful. Data sharing by design and data sovereignty are some of the main principles that will apply from the early stages ensuring cost effective and sustainable infrastructures that will drive Europe towards a single data market and green economic growth. This talk will present how to engage with the project, the design methodology, progress towards the roadmap for deployment and the collaborative approach to building data spaces in conjunction with all the sectoral data spaces and the Data Space Support Centre.

Data spaces, a novel concept pushing data sharing and exchange, are experiencing momentum because of recent developments motivated by the increasing need for interoperability and data sovereignty. After an initial phase, dating back to approximately twenty years ago, in which this concept has been tentatively explored in different scenarios, it is presently going through a consolidation phase in which both specifications and implementations converge towards a common reference for standardisation. In this context, we offer our view on data spaces by presenting a systematic literature survey, a description of the components needed to build them, how they work, and of existing mature software implementations. We thoroughly present the architectural vision behind the concept and we analyse the Reference Architectural Model by IDS. We provide practical pointers to readers interested in experimenting with software components used in data spaces, and we conclude by highlighting open challenges for their success.

The Green Deal Data Space is born in the big data paradigm where there is a variety of data formats and data models that are exposed as files or web APIs. As a result, we need to default in simple data structure that is transversal enough to be able to represent most of the more specific data models, formats and API payloads. Many data models present a structure that can be represented as tables.TAPIS stands for "Tables from APIS". It is a JavaScript code that uses a common data model that is an array of objects with a list of properties that can contain a simple or a complex value. In TAPIS offers a series of operations that use one or more arrays of objects as inputs and produce a new array of objects as an output. There are operations that create the arrays of objects from files or API queries (a.k.a. data import), others that manipulate the objects (e.g. merge two arrays in a single one) and some operations that generate visual representations of the common data structure including tabular, a map, a graph, etc.TAPIS is limited by its own data model. While many of the data models can be mapped to the common data model, a multidimensional data cube or a data tree cannot be represented in a single table in an efficient way. In the context of the Green Deal Data Space, most of the sensor data, statistical data, geospatial feature based data and administrative data can be considered object based data and can be used in TAPIS. TAPIS is able to connect to Sensor Things API (the sensor protocol selected in AD4GD and CitiObs), S3 buckets (the internal cloud repository used in AD4GD), GeoNetwork (the geospatial metadata catalogue selected in AD4GD and more4nature), and the OGC API features and derivates (the modern web API interfaces standardized by the OGC) but other data inputs will be incorporated, such as Citizen Science data sources and other popular APIs used in the more4nature project. More analytical functionalities are going to be incorporated in the CitiObs project. As part of the AD4GD Green Deal Information Model, there is an operation to associate semantics to each column of a table by linking it to a URI that defines the concept in an external vocabulary (as well as units of measure if appropriate). In order to be compatible with the data space architecture recommended by the International Data Space Association, we are working on supporting the catalogue of the Eclipse Data Connector, and to be able to negotiate a digital contract as a previous step to request access to the relevant data offered in the data space. To do so, we are working on incorporating the data space protocol as part of the TAPIS operations for data import. TAPIS is available as open source at https://github.com/joanma747/TAPIS.AD4GD, CitiObs and more4nature are Horizon Europe projects co-funded by the European Union, Switzerland and the United Kingdom.

Agricultural data privacy: Emerging platforms & strategies

• Offers a new vision on automated data harmonization in Dataspace (DS) systems. • Introduces LLM-based methods for scalable DS ingestion of heterogeneous datasources. • Presents a system with Harmonizer, Transformer, Evaluator components for ingestion. • Demonstrated an automated data ingestion prototype using LLM agents. • Validates the system with healthcare use case harmonizing heterogeneous data sources. Dataspaces (DS) enable stakeholders to collaborate on innovative, data-driven services by integrating data across domains. However, the realization and adoption of DS remain challenging due to domain-specific heterogeneity at the system, service, and data levels. While system and service-level heterogeneity can often be addressed through standards, data-level heterogeneity, namely data structures and semantics variations, remains challenging. To effectively ingest data into the DS, two communication endpoints must correctly interpret each other’s data models, therefore, DS ecosystems rely on “harmonization”, the process of generating a unified target data model from heterogeneous sources and transforming incoming data accordingly. Currently, harmonization and transformation are performed manually whenever new data sources are integrated. This is time-consuming, costly, and difficult to scale, posing a critical barrier to the realization and adoption of DS in practice. This study proposes a novel methodology for automated data harmonization during ingestion into DS ecosystems. The approach integrates harmonization, transformation, and human-in-the-loop evaluation within an automated system powered by modern LLM-based AI agents. These agents address data-level heterogeneity and generate harmonized target data models, representing a substantial departure from current manually-handled data harmonization. The system is validated through a healthcare use case, demonstrating its practical feasibility for harmonization during data ingestion into the DS. Overall, this work provides a foundational step toward seamless, efficient, and scalable data integration in DS. By automating data harmonization, it delivers substantial value to industry digital solutions as well as domains where data heterogeneity persists, including IoT or Big Data platforms.

This minitrack, for the second time, deepens understanding of data sharing, data ecosystems, data platforms, data spaces, data hubs, and repositories.It emphasizes diverse perspectives, covering technical, organizational, social, and regulatory aspects, and examines multiple levels, including system, individual, community, and societal dimensions.This minitrack fosters discussions on emerging technologies and is open to insights on repurposing existing technologies for data-driven care coordination and research, involving increasing data over extended periods.

This paper focuses on the emerging framework for non-personal data protection in India, analysing its potential benefits and drawbacks. The Indian government has used a diverse range of policy instruments to develop a techno-political framework for non-personal data regulation, wherein non-personal data are predominantly viewed as a national economic resource that must be shared to promote domestic players and reinforce India’s digital and data sovereignty. Despite several of its innovative features, including instituting nationwide platforms for non-personal data sharing, this framework lacks clarity regarding data intermediaries’ roles, provides insufficient privacy protections, and creates several business uncertainties. India’s experience reflects developing countries’ challenges and dilemmas in regulating non-personal data, especially balancing economic goals with privacy concerns and managing diverse stakeholder interests. We conclude by highlighting the importance of a rights-based approach with clear accountability mechanisms and streamlined policy/legal frameworks to ensure effective and equitable non-personal data regulation in developing countries.

With the introduction of Industry 4.0 technological concepts, suppliers and manufacturers envision new or improved products and services, cost reductions, and productivity gains. In this context, data exchanges between companies in the same or different activity sectors are necessary, while assuring data security and sovereignty. Thus, it is crucial to select and implement adequate standards which enable the interconnection requirements between companies and also feature security by design.The International Data Spaces (IDS) is a current standard that provides data sharing through data spaces mainly composed of homogeneous rules, certified data providers/consumers, and reliability between partners. Implementing IDS in sectors such as textile and clothing is expected to open new opportunities and challenges.This paper proposes a prototype for the IDS Security Components in the Textile and Clothing Industry context. This prototype assures data sovereignty and enables the interactions required by all participants in this supply chain industry using secure communications. The adoption of IDS as a base model in this activity sector fosters productive collaboration, lowers entry barriers for business partnerships, and enables an innovation environment.KeywordsIDSDAPSConnectorsSecurityIndustry 4.0

With the continuous increase in the number of motor vehicles, the per capita ownership also increases, and the demand for second-hand car circulation is increasing, but the price of second-hand cars is difficult to accurately estimate and set, which is not only affected by the basic configuration of the car itself, but also by factors such as the condition of the car. This article will help used car trading platforms to solve this problem through data analysis and modeling, and build a machine learning model using a large amount of historical used car transaction data, and analyze the modeling process in detail from data preprocessing, feature engineering, and modeling parameter adjustment. Three machine learning models with good performance in regression problems, namely gradient lifting decision tree GBRT, ultimate gradient lifting algorithm XGBoost and light gradient lifting machine LightGBM, were fused by Stacking model fusion method. The simulation data shows that the average absolute error of the Stacking model fusion is reduced by 2.7% compared with the single model with the best prediction performance, which can be used to solve the practical problem that the price of used cars is difficult to estimate.

Emergency response requires an efficient information supply chain for the smooth operations of intra- and inter-organizational emergency management processes. However, the breakdown of this information supply chain due to the lack of consistent data standards presents a significant problem. In this paper, we adopt a theory- driven novel approach to develop an XML-based data model that prescribes a comprehensive set of data standards (semantics and internal structures) for emergency management to better address the challenges of information interoperability. Actual documents currently being used in mitigating chemical emergencies from a large number of incidents are used in the analysis stage. The data model development is guided by Activity Theory and is validated through a RFC-like process used in standards development. This paper applies the standards to the real case of a chemical incident scenario. Further, it complies with the national leading initiatives in emergency standards (National Information Exchange Model)

With the advancement of various technologies, the research and application of space object optical characteristic (SOOC), one of the main characteristics of space objects, are faced with new challenges. Current diverse structures of massive SOOC data cannot be stored and retrieved effectively. Moreover, SOOC processing and application platforms are inconvenient to build and deploy, while researchers’ innovative algorithms cannot be applied effectively, thereby limiting the promotion of the research achievements. To provide a scaffolding platform for users with different needs, this paper proposes SOOCP, a SOOC data and analysis service platform based on microservice architecture. Using the hybrid Structured Query Language (SQL)/NoSQL service, the platform provides efficient data storage and retrieval services for users at different levels. For promoting research achievements and reusing existing online services, the proposed heterogeneous function integration service assists researchers and developers in independently integrating algorithmic modules, functional modules, and existing online services to meet high concurrency requests with a unified interface. To evaluate the platform, three research cases with different requirement levels were considered. The results showed that SOOCP performs well by providing various data and function integration services for different levels of demand.

This article highlights how action research (AR) effectively supports the establishment and further development of an AI-supported system of systems lifecycle management with digital threads. The starting point is the growing complexity of networked product and system landscapes, in which technical, organizational, and data related issues are inextricably intertwined. AR combines planned intervention with systematic observation and joint reflection in real business environments. In several industry academic collaborations, recurring cycles of diagnosis, planning, intervention, and reflection were used to integrate heterogeneous processes and data models across the entire lifecycle: from CAD CAE PDM couplings to endtoend change processes to MBSE embeddings with SysML and model driven mechatronics. The results show three core effects. First, AR promotes organizationwide learning: boundaries between mechanics, electronics, software and IT are dissolving, acceptance of new roles and data sovereignty is increasing, and understanding of quality, consistency, and traceability is growing. Second, AR facilitates gradual technical integration because social negotiation and technical architecture are designed together including links to IoT MES data, digital twin approaches, and sustainability metrics (e.g., LCA integration into development decisions). Third, measurable improvements are emerging throughout the lifecycle, such as shorter lead times, more robust change statuses, and traceable thread links across domains and organizations. At the same time, the limitations become transparent: case transferability remains limited, the balance between participation and analytical distance requires clear roles, and SoS constellations with many partners require patience and reliable governance. A practical design framework can be derived from the overall view: AR serves as a sociotechnical catalyst that combines MBSE, digital threads, and sustainability into a learning system. Early joint hypothesis formation, iterative piloting with clear maturity criteria, the coupling of qualitative reflection and quantitative indicators, and the anchoring of communities of practice are crucial to success. In this way, AR not only enables the introduction of new tools, but also the transformation of the development culture a prerequisite for ensuring SoS capability, transparency, and adaptability in the digital age.

The results of the research are presented in six main parts, as follows: “Information space and new patterns of data and information organization”, “Information strategies, social networks and influence in cyberspace”, “Hybrid conflicts and new instruments of power in cyberspace”, “New instruments of power: hybrid threats and hybrid operations”, “Hybrid operations in hybrid conflicts and hybrid warfare” and “New patterns of prevention and defence against hybrid threats”. In the first part, social networks are considered as powerful and effective means of communication and media for global, mass, networked, direct and anonymous communication, which are used in cyberspace to effectively impose ideas, ideologies and create and spread disinformation. The second part describes the adjustment of information strategies and information operations, and describes new patterns of planning, implementation and operation of psychological and media operations in which various actors (state, non-state and corporate) use artificial intelligence technologies on social networks for their own needs and interests. The third part of the dissertation presents the causes and occurrence of hybrid conflicts, their definitions and main features. Then, hybrid threats and hybrid operations of influence are presented, as new instruments of power in cyberspace that have emerged as a result of cyberspace misuse and artificial intelligence technologies on social networks. In this part of the research, we observe social networks as means of planning and conducting hybrid influence operations, which we consider here as the main information instrument of hybrid power, that achieves the strongest effects through cyberspace and serves to create strong and effective disinformation and other types of hybrid threats. The fifth part of the dissertation presents the results of research related to a representative sample of known and recognized examples of misuse of artificial intelligence technologies on social networks for planning and conducting hybrid impact operations. The primary scope of research relates to the hybrid influence operations carried out by the United States and the Russian Federation in different geographical areas and in different contexts. The sixth part of the dissertation proposes new patterns of prevention and defence against hybrid threats and operations, the creation of a new information strategy, and defence mechanisms and models. The end of this section points out the importance and the role of building digital and data sovereignty. The research confirmed the main hypothesis. Artificial intelligence and social networking technologies in hybrid conflicts are used as powerful and efficient tactical tools, with potential strategic consequences for the body of public knowledge of target audiences, in order to (re)shape it according to the needs of the information attacker. We have demonstrated that these are psychological operations of a strategic nature, in which artificial intelligence technologies and all social networks are used as dominant and effective tactical means, in a spatially and temporally unlimited range, to create disinformation and other various types of hybrid threats and constant (24/7) informationpsychological pressures directed at target audiences (individually, in groups or en masse) depending on the needs and goals of the information attacker.

This study develops a literature-driven conceptual framework for energy-burden-aware underwriting within the context of U.S. residential decarbonization finance. It addresses a critical and growing disconnect between the policy-driven urgency to decarbonize residential buildings and the financial constraints faced by households most in need of retrofit investments. Notably, those households that stand to benefit the most from energy-efficiency improvements are often underserved by traditional underwriting models. Conventional lending frameworks prioritize stable income streams, established credit histories, and property-based collateral signals, whereas energy-burdened households—characterized by high utility costs, inefficient housing conditions, and constrained liquidity—exhibit affordability dynamics that are poorly captured by these models but are highly relevant for socially beneficial investment. Drawing upon interdisciplinary literature spanning energy burden, building decarbonization, alternative data underwriting, explainable artificial intelligence, and algorithmic accountability, this paper proposes an AI-enabled underwriting framework that integrates utility-bill histories, housing attributes, retrofit cost-benefit dynamics, and consumer protection considerations. Importantly, the framework emphasizes transparency and interpretability, avoiding reliance on opaque risk-scoring mechanisms. Methodologically, the study employs a structured integrative review of academic and policy-oriented sources, synthesizing insights into a multimodal framework encompassing data governance, feature engineering, model development, explainability outputs, fairness diagnostics, and financing pathway design. The contributions of this research are threefold. First, it reconceptualizes utility payment data from a signal of financial distress into a meaningful input for transition finance. Second, it establishes a direct linkage between household energy burden and underwriting design principles. Third, it outlines governance and accountability conditions under which AI-assisted retrofit financing can advance decarbonization objectives without reinforcing systemic exclusion. Given the absence of original empirical data, the findings are presented as a conceptual architecture, evaluation framework, and forward-looking research agenda rather than as empirical validation. The proposed framework is intended to guide future implementation and testing by financial institutions, utilities, public green banks, and housing policy stakeholders. paper develops a literature-based conceptual framework for energy-burden-aware underwriting in U.S. residential decarbonization finance. The study addresses a widening disconnect between the climate-policy imperative to decarbonize homes and the financial reality that many households most likely to benefit from retrofit investment are also the least well served by conventional underwriting. Existing lending models privilege stable income, established credit files, and property value signals, whereas the households facing high utility costs, inefficient housing, and limited liquidity often present precisely the affordability patterns that make retrofit investment socially valuable. Drawing on scholarship on energy burden, building decarbonization, alternative-data underwriting, explainable artificial intelligence, and algorithmic accountability, the paper proposes an AI framework that uses utility-bill histories, housing characteristics, retrofit economics, and consumer-protection rules to assess payment feasibility without reducing underwriting to opaque risk scoring. Methodologically, the paper uses a structured integrative review academic and policy sources and synthesizes them into a multimodal framework comprising data governance, feature design, model development, explanation outputs, fairness diagnostics, and financing-channel selection. The paper’s contribution is threefold: it reframes utility bills from a distress signal into a transition-finance input; it links household energy burden to underwriting design; and it specifies governance conditions under which AI-assisted retrofit finance may advance decarbonization without reproducing exclusion. Because no original dataset is analyzed, the results are presented as a conceptual model, evaluation logic, and research agenda rather than empirical claims. The framework is intended to support future testing by lenders, utilities, public green banks, and housing-policy practitioners.

The EC-funded iMagine project aims to revolutionise aquatic science research by providing open access to AI-powered image analysis tools and resources. Focused on the theme of "Healthy Oceans, Seas, Coastal and Inland Waters," iMagine addresses the growing need for efficient analysis of vast amounts of image data generated from diverse sources like underwater cameras, drones, microscopes, and satellites.iMagine operates the iMagine AI Platform", a computational platform built upon the AI4OS framework and supported by AI4EOSC. Hosted on OpenStack clouds within the EGI e-Infrastructure Federation, the platform offers significant GPU and storage capacity to handle the dynamic needs of various research projects. It provides a complete suite of tools for the entire machine learning lifecycle, including image annotation, preprocessing, a deep learning model catalogue, model training and evaluation, and model inference for scientific end-users. This comprehensive approach facilitates collaboration and knowledge sharing between AI experts and aquatic science researchers.The platform's core functionalities include:A generic computational platform supporting the development of AI-based image analysis services for the aquatic science community. Development of AI-based image analysis services addressing various scientific challenges within aquatic research. Provision of labelled image datasets, enabling AI models' training and retraining. Sharing of best practices, disseminating knowledge related to imaging data and AI-driven image analysis in aquatic sciences. iMagine supports a diverse range of use cases, demonstrating the power of AI for image analysis in aquatic research. These use cases tackle critical issues including floating litter classification and quantification, plankton taxonomic identification, ecosystem statistics generation, oil spill movement and spread prediction, underwater audio data analysis to track vessel activity, and coral reef health monitoring.The iMagine Competence Centre, consisting of AI experts, domain scientists, and image data owners, facilitates collaboration between use cases and platform providers. The Competence Centre organises regular meetings, training sessions, and feedback collection to refine AI models and ensure the development of robust online services for end-users.To further enhance data quality, reproducibility, and scientific progress, iMagine adheres to best practices in data management, quality control, and model development. All use cases contribute to publicly available image datasets on Zenodo, allowing for model validation, retraining, and the development of new models. iMagine actively collaborates with other prominent projects like EOSC, AI4EU, and Blue-Cloud 2026 to maximise its impact and promote the broader adoption of AI-powered solutions within the aquatic science community.