Tag: Big Data

  • Data Space Technologies

    Data Space Technologies

    Haukaas C.A., Fredriksen P.M., Abie H., Pirbhulal S., Katsikas S., Lech C.T., Roman D. (2025). “INN-the-Loop: Human-Guided Artificial Intelligence.” 12-14.

    Data space technologies are key enablers of AI and data-driven value creation because they address fundamental challenges with data system integration, data curation, verifiability, security, and privacy.

    A data space consists of common standards for organizing and exchanging data and a set of technologies that adhere to those standards. Data space technologies can be open-source or proprietary, but they must adhere to common data spaces standards and rules. Common standards are needed to ensure that systems and data are interoperable, and can utilize common infrastructure and services, for example to manage identities, system access and data exchange in compliance with European digital regulations.

    Data space technologies with digital trust management frameworks and digital marketplaces are being developed to enable a more open and equitable digital ecosystems in Europe, where data and digital assets can be securely exchanged, reused and improved over time.

    The International Data Spaces Association, Gaia-X, FIWARE, Big Data Value Association and OASC (Open and Agile Smart Cities and Communities) are a few examples of organizations that collectively represent more than 1000 member organisations, 400+ cities, and 100+ national hubs in Europe, Asia, and the Americas that are working on projects to develop common data space architectures and technologies.

    Smart city and community initiatives are particularly relevant for Data Spaces because cities, regions, municipalities, and the public sector need to continuously improve the cost-effectiveness of services across several critical sectors. AI has great potential to support increased productivity, sustainability and community engagement in digital and green transformation, but solutions are needed to enable secure data exchange and deployment of trustworthy AI across critical sectors and regions. The Living in EU initiative aims to promote citizen-centric collaboration and re-use of solutions, products, and services across a common digital market to avoid duplicating efforts and expenditure that result in data silos and fragmented infrastructure. Living in EU is promoted the European Commission, the European Committee of Regions, The Council of European Municipalities and Regions (CEMR), The European Regions Research and Innovation Network (ERRIN), The European Network of Living Labs (ENoLL), OASC, and Eurocities, a network of over 200 of Europe’s largest cities representing over 150 million people across 38 countries.

    A data space consists of tools that adhere to common standards defined in the Data Space Blueprint:

    • DSSC Blueprint: The Data Space Support Center (DSSC) blueprint is a comprehensive set of guidelines to support implementation, deployment and management of data spaces. The Blueprint consists of key concepts, a starter kit, glossary, a collection of data space standards and the following organisation and technical building blocks (DSSC 2024)[i]:
      • Business, governance and legal building blocks provide guidance to new entrants and operators of infrastructure, software, services and technologies that comply with data spaces standards. This support includes but not limited to guidance on choices in design of business model, data products, organisational form, regulatory compliance and contractual frameworks that are supported by services and software.
      • Technical building blocks are divided into foundational standards, control and data planes for exchanging data, and data space services for implementing the technical building blocks. These standards for technologies and services are designed to ensure data interoperability, data sovereignty and trust, and provide enablers for value creation from data, which is one of the ultimate goals of a data space (DSSC 2024).
    • Decentralised identifiers (DID) and verifiable credentials (VC): a key technical building block for Data Spaces is the DID standard developed by the World Wide Web Consortium (W3C), an international standards organization founded in 1994 by Tim-Berners Lee, the inventor of the world wide web. A DID is a universal resource identifier (URI) for an entity (e.g., a person, organization, thing, concept, data model, algorithm, abstract entity, etc.) (W3C 2022).[ii] URIs are used to organize data and services in standardized machine-readable ontologies and catalogues. This enables systems find information and navigate ontologies and data catalogues across large networks of distributed systems. A decentralised identifier (DID) goes a step further by providing a method to prove ownership/control over an entity/subject/concept. A DID points to a DID document that uses cryptographic mechanisms to verify credentials related to ownership and rights to create, access and modify information. This enables a controlling entity to create and modify their own universal identifiers independent of centralised registries because the controlling entity can use verifiable credentials (VCs) to prove their own identity and to prove their rights to create, modify and access information that is represented by the DID. VCs provide a set of tamper-evident claims, which supports verifiability, traceability and accountability in digital information, also known as data provenance (W3C 2025).[iii] This independent control over verifiable information is known as self-sovereignty and self-sovereign identity (SSI), and it has potential to revolutionize the internet by making more information verifiable, machine-readable and more easily discoverable across decentralised systems, provided that common semantic web standards are followed for organizing and accessing information.
    • Privacy-enhancing technologies (PETs): A DID document can have one or more different representations of information describing a past, current, or desired state of the DID subject. The ability to provide multiple representations of information is an enabler for PETs because a DID document can utilize different methods for sharing verifiable information without necessarily transferring data or revealing underlying data. One example is secure multi-party computation (SMPC) with full homomorphic encryption, which was used by two European hospitals in a pilot project of the European Health Data Space to securely analyse health data for cancer patients without transferring underlying health data to the hospitals (Ballhausen 2024).[iv] Another example is a zero-knowledge proof (ZKP) to prove that a person has a required credential, such as an education certificate, valid driver’s license, or fulfils a minimum age requirement, without revealing details of the person’s age, date of birth, address, or other unnecessary information. DIDs can also strengthen privacy and security by using attribute-based encryption and access control to authorize access to specific information based on a dynamic set of conditions, such as the privacy preferences of the DID owner and levels of digital trust or cyber risk to systems handling information in the digital value chain. In summary, technical standards for DIDs, VCs, and Data Spaces, in combination with EU digital regulations, create a great opportunity for innovation in PETs to address security and privacy risks of AI-enabled systems in critical sectors.
    • Technical standards have been collected and organized into the following categories:
      • Data Interoperability standards
      • Data Sovereignty and Trust standards
      • Data Value Creation standards
    • DSSC Toolbox: The Toolbox is a curated catalogue of solution implementations (software and non-software tools) that are aligned with the DSSC Blueprint and have passed the Toolbox validation scheme. The
      • Toolbox contains open and closed solutions for technical and organisational functionalities and can be accessed as data space services (DSSC 2024).
      • The Toolbox validation scheme is a self-assessment scheme that enables new solutions and solution providers to be listed in the Toolbox.
    • A digital marketplace is a common way to generate value in a data space (DSSC 2024).[1] The DS Blueprint describes functional specifications for digital marketplaces as part of the Data Value Creation standard and technical building block. The standard enables secure and efficient data exchange and digital transactions using advanced features for data catalogue management with DIDs. A data catalogue using DIDs makes product offerings machine-readable and more easily discoverable within a data space and across data spaces and marketplaces. A marketplace can also “establish a trusted relationship between a data product provider and any user who has searched, found and selected one or more data products from this provider in the data space. It provides the tools required to negotiate conditions for the delivery and use of the products, monitor the process and store all the relevant information, i.e. everything needed to ensure the journey of the provider and the user goes smoothly.” (DSSC 2024).
    • Minimum Interoperability Mechanisms (MIMs) are being developed by the OASC in a standard recommendation to the ITU Telecommunications Standardization Sector (ITU-T) to support data interoperability in Data Spaces for Sustainable and Smart Cities and Communities (DS4SSCC) and ensure compliance with the EU Interoperability Act (EC 2024)[2]. The MIMs Overview provides a description of the concept and role of the following MIMs (OASC 2024)[3]:
      • MIM 1: Context Information
      • MIM 2: Data Models
      • MIM 3: Contracts
      • MIM 4: Trust
      • MIM 5: Transparency
      • MIM 6: Security
      • MIM 7: Places
      • MIM 8: Indicators
      • MIM 9: Analytics
      • MIM 10: Resources
    • MIMs Resources provide additional support for public sector and local administrations in cities and smart communities to learn and experiment with digital transformation initiatives:
      • CITYxCITY Academy: includes an online portal with access to experts, tools and courses.
      • CITYxCITY Catalogue: global collection of deployed solutions, products and best practice.
      • CITYxCITY Festival: annual networking event for the OASC community.
      • Living-in.EU MIMs Plus: an expansion of MIMs with additional technical stacks, tools and management standards for local administrations intended to support broad up-scaling of digital transformation projects in line with the Living in EU initiative, which aims to serve 300 million Europeans. The ‘plus’ banner refers to European specifications and initiatives, such as EIF4SCC, ISA2, CEF, INSPIRE, EIP-SCC, ELISA, LORDI, DIGISER (OASC 2022)[v].

    For smaller organisations, such as startups, SMEs and municipalities, data spaces can eliminate the need to make large upfront investments in digital infrastructure for advanced digital platforms and digital twins. Open-source technologies and smart data models can be reused as a foundation platform, instead of reinventing systems, data models, communications protocols, services, and security controls. This frees more time and financing to focus on value-creation, paying startups and smaller specialist service providers to integrate components and customize software and user interfaces to customer needs.

    The concept of distributed computing is not new, but what distinguishes European data space initiatives from hyperscaler ecosystems is common technical standards to ensure interoperability that reduce vendor lock-in, and enable collaboration to improve cybersecurity, data integrity, and fair economic value creation, while complying with important EU digital regulations for privacy, safety, and cyber resilience.

    [i] DSSC (Data Spaces Support Centre) (2024). Data Spaces Blueprint v1.5. Data Spaces Support Centre. https://dssc.eu/space/bv15e/766061169/Data+Spaces+Blueprint+v1.5+-+Home

    . Accessed 22.01.2025.

    [1] DSSC (Data Spaces Support Centre) (2024). Data Spaces Blueprint v1.5. Data Spaces Support Centre. https://dssc.eu/space/BVE/357076678/Marketplace+Functional+Specifications. Accessed 22.01.2025.
    [2] European Commission. Press release 26.08.2024. Minimal Interoperability Mechanisms: Advancing Europe’s digital future. https://data.europa.eu/en/news-events/news/minimal-interoperability-mechanisms-advancing-europes-digital-future
    [3] Open and Agile Smart Cities and Communities (OASC) (2024). Draft Recommention ITU-T Y.MIM. May 2024. https://mims.oascities.org/mims/y.mim-overview
    [i] DSSC (Data Spaces Support Centre) (2024). Data Spaces Blueprint v1.5. Data Spaces Support Centre. https://dssc.eu/space/bv15e/766061169/Data+Spaces+Blueprint+v1.5+-+Home. Accessed 22.01.2025.

    [ii] Sporny M., Longley D., Sabadello M., Reed D., Steele O., Allen C.; World-Wide Web Consortium (W3C) (2022). Decentralized Identifiers (DIDs) v1.0. W3C Recommendation 19.07.2022. https://www.w3.org/TR/did-core/

    [iii] Sporny M., Longley D., Chadwick D., Herman I.; World-Wide Web Consortium (W3C) (2025). Verifiable Credentials Data Model v2.0. W3C Candidate Recommendation Draft. 27.01.2025. https://www.w3.org/TR/vc-data-model-2.0/

    [iv] Ballhausen, H., Corradini, S., Belka, C. et al. (2024). Privacy-friendly evaluation of patient data with secure multiparty computation in a European pilot study. npj Digit. Med. 7, 280 (2024). https://doi.org/10.1038/s41746-024-01293-4

    [v] LI.EU Technical sub-group chaired by OASC (2022). MIMs Plus version 5.0 final draft. June 2022. https://living-in.eu/mimsplus

  • Institutional complexity and governance in open-source ecosystems: A case study of the oil and gas industry

    Institutional complexity and governance in open-source ecosystems: A case study of the oil and gas industry

    Mahdis Moradi, Vidar Hepsø, Per Morten Schiefloe,
    Institutional complexity and governance in open-source ecosystems: A case study of the oil and gas industry,
    Journal of Innovation & Knowledge, Volume 9, Issue 3, 2024, 100523, ISSN 2444-569X, https://doi.org/10.1016/j.jik.2024.100523.
    (https://www.sciencedirect.com/science/article/pii/S2444569X24000623)

    Abstract

    There has been a growing interest in open-source innovation and collaborative software development ecosystems in recent years, particularly in industries dominated by intellectual property and proprietary practices.

    However, consortiums engaged in these collaborative efforts often face difficulties in effectively balancing the competing dynamics of trust and power. Collaborative knowledge creation is pivotal in ensuring long-term sustainability of the ecosystem; knowledge sharing can take place by steering trust judgments toward fostering reciprocity.

    Drawing on a longitudinal case study of the Open Subsurface Data Universe ecosystem, we investigate the intricate interplay between trust and power and its pivotal influence on ecosystem governance. Our investigation charts the trajectory of trust and power institutionalization and reveals how it synergistically contributes to the emergence of comprehensive hybrid governance strategies.

    We make the following two contributions to extant research. First, we elucidate a perspective on the conceptual interplay between power and trust, conceiving these notions as mutual substitutes and complements. Together, they synergistically foster the institutionalization and dynamic governance processes in open-source ecosystems. Second, we contribute to the governance literature by emphasizing the significance of viewing governance as a configuration of institutionalization processes and highlighting the creation of hybrid forms of governance in complex innovation initiatives.

    Keywords: Open source; Innovation; Cocreation; Governance; Institutional trust; Power

    Visit the article webpage
  • FAME: Federated decentralized trusted dAta Marketplace for Embedded finance

    FAME: Federated decentralized trusted dAta Marketplace for Embedded finance

    January 1, 2023 @ 8:00 am – December 31, 2025 @ 5:00 pm CET

    (PROJECT)

    FAME. “FAME.” Accessed 13.08.2025. https://www.fame-horizon.eu.

    FAME is a joint effort of world-class experts in data management, data technologies, the data economy, and digital finance, aiming to develop and launch to the global market a unique, trustworthy, energy-efficient, and secure federated data marketplace for Embedded Finance (EmFi).

    ​The FAME project has received funding from the European Union’s Horizon 2023 Research and Innovation Programe under grant agreement nª 101092639. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or Horizon Europe. Neither the European Union nor the granting authority can be held responsible for them.

    Marketplace

    The FAME Federated Data Marketplace for Embedded Finance is a Data Space, according to the definition of the EU, customized for buying and selling federated data assets in the financial sector.

    Accessible through a unified entry point, the Marketplace allows for secure data access, sharing, trading and analysis through FAME’s analytical tools for both finance and non-finance organizations, tech and non-tech users, and other end-users.

    Why FAME?

    Modern data marketplaces are transforming how data assets are shared, traded, and utilized. Recent European initiatives have made significant strides, particularly in enhancing data monetization, regulatory compliance, and secure data exchange. However, existing centralized marketplaces face challenges that limit broader participation and accessibility. Notable limitations include complex data discovery processes, limited transparency in value-based data monetization, and insufficient integration of trusted, energy-efficient analytics. Addressing these gaps can unlock new data-driven applications in sectors like finance, retail, and smart cities, empowering innovative services that seamlessly integrate financial data. That is where FAME comes in.

    Visit the project website
  • enRichMyData

    enRichMyData

    October 21, 2022 @ 8:00 am – September 30, 2025 @ 5:00 pm CEST

    (PROJECT)

    enRichMyData. “enRichMyData.” Accessed 13.08.2025. https://enrichmydata.eu.

    enRichMyData develops a novel paradigm for building rich, high-quality and valuable datasets to feed Big Data Analytics and AI applications. It has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No 101070284.

    The enRichMyData project is coordinated by SINTEF (Norway), one of European’s largest independent research organisations. The project partners include companies such as Philips (The Netherlands) and Bosch (Germany), dedicated to engineering and manufacturing; Speed Network (Estonia), a provider of procurement data; JOT Internet Media (Spain), a digital marketing company; CS Group (Romania), a software service company; Expert AI (Italy), a technology company specializing in natural language understanding; and Ontotext (Bulgaria), a semantic technology company. They will have the full support of the research partners that, in addition to SINTEF, include the University of Milano Bicocca (Italy), Jozef Stefan Institute (Slovenia), University of Copenhagen (Denmark), GATE Institute (Bulgaria), and BGRIMM Technology Group (China).

    enRichMyData delivers an open software toolbox – the enRichMyData toolbox – comprising practical, robust and scalable components to support organizations in enriching their data with reference data they may have limited knowledge of, as well as supporting data providers in making their data reusable and available in data enrichment processes.

    The toolbox lowers the technological entry barriers by providing support for the definition of highly scalable and replicable data enrichment pipelines through a set of tools and infrastructure services related to capabilities needed during the lifecycle of enrichment pipelines. The toolbox makes the data enrichment process accessible to a broader set of stakeholders by reducing the required expertise and enhancing the tool support level.

    Empower AI-driven business products and services

    h-quality, rich and meaningful data are crucial to successfully implementing Artificial Intelligence (AI) and Big Data Analytics (BDA) solutions. Delivering required data to feed into AI and BDA models is costly, difficult, and often limited in data and skill availability. It is well known that up to 80% of the effort spent in AI and BDA projects is dedicated to ensuring data is fit for purpose. Activities are required to discover, understand, select, clean, transform, and integrate data from a variety of sources in such a way that data can be fed into the modelling phase. Such activities result in enriched data, eventually improving the quality of downstream BDA and AI applications. The data enrichment process is implemented by specifying, deploying, and executing data enrichment pipelines over data that can be structured, semi-structured and unstructured, in large amounts, and from static or streaming sources. While techniques exist to cover different enrichment operations such as data cleaning, linking, feature extraction, classification and semantic annotation, etc., the lack of comprehensive approaches and established tools dedicated to data enrichment makes the definition, implementation, and operation of enrichment pipelines difficult for too many organizations willing to improve their BDA and AI applications.

    The overall vision of the enRichMyData project is to create a novel paradigm for building rich, high-quality, valuable, and FAIR-compliant datasets to feed downstream BDA and AI applications in the context of data-sharing ecosystems, such as data spaces. The paradigm facilitates the specification and execution of data enrichment pipelines, focusing on supporting various data enrichment operations. enRichMyData makes this easily accessible to a wide set of large and small organizations that encounter difficulties in delivering suitable data to feed their BDA and AI solutions due to the lack of usable tools/expertise for the cost-effective management of data enrichment pipelines.

    SINTEF

    View Organizer Website

    Visit the project website
  • VentureNet: Reducing barriers to innovation and business development through secure data exchange

    VentureNet: Reducing barriers to innovation and business development through secure data exchange

    September 1, 2022 @ 8:00 am – September 30, 2023 @ 8:00 am CEST

    (PROJECT)

    The VentureNet data exchange project was a research project completed in 2023 in collaboration with SINTEF and the Norwegian Centre for Research and Innovation in Cybersecurity in Critical Sectors (SFI NORCICS) based at the Norwegian University of Science and Technology (NTNU). The project received co-financing from the Research Council of Norway and Innlandet County through the FORREGION program.

    Background

    A major EU industrial research project on data harmonization, euBusinessGraph, identified major challenges with access to business data, citing that “it is extremely expensive, time consuming and error prone to find, interpret and reconcile”, especially across industries, geographic regions and languages.

    Machine learning (ML) plays an important role in automating data processing tasks such as natural language processing (NLP), but human input is needed to train ML algorithms and thereby improve the accuracy of ML models. One of the challenges identified by data scientists is a lack of machine learning models with humans-in-the-loop (HITL). 80% of the cost of Big Data and Artificial Intelligence (AI) projects is time spent by data scientists finding, interpreting and harmonizing data. A lack of HITL limits the effectiveness of ML models and their potential to reduce cost in Big Data and AI projects.

    VentureNet aims to create a virtual business accelerator hub powered by its proprietary Information Management System (IMS), which centralizes access to multiple digital ecosystems, facilitating data exchange and reducing manual data entry. Developed in collaboration with SINTEF and NTNU, the project focuses on creating a secure data exchange framework with Human-in-the-Loop (HITL) capabilities that can be scaled up in European data markets.

    Project Results

    The project delivered a report introducing a novel framework and new technical solution for data exchange that will facilitate dynamic harmonization and enrichment of business data with HITL ML models. The envisioned solution is a digital B2B workspace and marketplace that will automate data management tasks and processes and facilitate involvement of non-technical data providers (small businesses) in data enrichment with HITL models.

    SINTEF compared VentureNet’s IMS to leading low-code platforms and AI solutions, such as Oracle APEX, AWS Amplify, Open AI GPT, Sheet GPT, and Google Natural Language API and concluded the following:

    We compared VentureNet against a few low-code/no-code platforms. Low-code/no-code platforms significantly accelerate software development by simplifying and automating code generation, making it possible for non-developers to create applications, thereby democratizing development and reducing the technical skill barrier. However, these platforms can limit customization capabilities, restrict advanced functionality, and lead to vendor lock-in situations, potentially impeding scalability, and long-term growth for more complex or unique business requirements.

    Potential for higher accuracy and filtering of hallucinations, using HITL, especially in sensitive domains, such as healthcare.

    The principal advantage of the VentureNet platform lies in its comprehensive integration capabilities. It can fulfill all project requirements, including form processing, API management, low-code graphical user interface elements, and data enrichment. Unlike other generic platforms, VentureNet enables the development of highly customized solutions tailored to specific project needs.

    Although it is possible to assemble similar platforms using excellent off-the-shelf tools, coordinating, and integrating these disparate components requires significant time and effort. Additionally, the complexities associated with billing processes can often pose substantial challenges. VentureNet solves these concerns by offering a unique solution that simplifies project management and reduces operational overhead. This approach ensures a streamlined workflow and efficient project execution.

    Contact VentureNet to learn more about the project results and collaboration opportunities.

    VentureNet

  • Digitalization, innovation and network effects in rural and mountainous regions: a study of the status quo and opportunities for the IMS

    Digitalization, innovation and network effects in rural and mountainous regions: a study of the status quo and opportunities for the IMS

    June 1, 2021 @ 8:00 am – May 31, 2022 @ 5:00 pm CEST

    (PROJECT)

    The IMS project was completed in 2022 with support from the Eastern Norway Research Institute (a division of the University of Inland Norway) and SINTEF, one of Europe largest independent research institutions. The project was managed by an affiliate of VentureNet, which VentureNet acquired in 2023, and the project received co-financing from the Research Council of Norway and Innlandet County through the FORREGION program.

    Background: small-and-medium-enterprises (SMEs) need help with data management.

    Problem: Limited availability and access to private sector (business) data.

    • High cost of generating business data and analysis for startups and SMEs.
    • High cost of harmonizing business data using Big Data and artificial intelligence (AI).

    Opportunities: make business analysis more cost-effective for SMEs using Big Data.

    • “Core” business data points are reused in sales, fundraising and other processes.
    • Gather existing “core” business data using Big Data to save time and cost for SMEs and advisors.
    • Exchange “core” business data across digital platforms to save cost and generate revenue for SMEs, advisors, customers, investors, making SME data more valuable and cost-effective to analyze.

    Challenges required advanced knowledge of regional dynamics and data science.

    • Lack of digital platforms designed to make use of business data exchange and virtual networking.
    • How to build a network of businesses and advisors that benefit from enriching and exchanging data.
    • Technical bottlenecks in Big Data related to data harmonization, enrichment and exchange.

    Project objective

    Inform design of a main research project to develop new machine-learning and data exchange models for Big Data and digital B2B platforms that accelerate digitalization, innovation and network effects for SMEs in rural areas.

    Hypotheses for lower growth rates in rural and mountainous regions:

    • The Lemon Problem: lower trust in SMEs due to a lack of verified data (“Information Asymmetry).
    • Lower access to capital and specialist expertise in non-metropolitan areas (“Agglomeration Effects”).
    • Lower growth ambition? The Inland region of Norway has a high proportion of micro-enterprises.
    • Is this due to a perception of lower access to private capital?
    • Possible lower access to specialist/M&A expertise?
    • Other factors, such as cultural factors, systemic inequalities or biases, or lower competitiveness?

    Project Results

    Key results from the University of Inland Norway

    • Statistical analysis of ICT companies in the Inland region using publicly available data from Proff.
    • Use of Centralization Index as a basis for future ethnogeography studies and statistical analysis.
    • Mapping of advisory services and financial services available for companies in Innlandet.
    • Questionnaire templates to evaluate knowledge and utilization of digital tools and advisory services.
    • Other relevant suggestions from HINN colleagues for use in future research projects:
      • Social network analysis and gender study methods to analyze cultural and systemic factors linked to growth.
      • Analysis of industry clusters and network effects to identify best practice and factors linked to value creation.

    Key results from SINTEF

    • Clarified that the IMS has an innovative approach to data enrichment, which can be used to develop advanced machine-learning models with Humans-In-The-Loop, a current challenge for data scientists.
    • Identified opportunities to reduce time and cost of data entry by using machine-learning techniques for data harmonization and enrichment.
    • Identified areas for further development: Data Fusion, Dynamic Data Enrichment, Data Exchange.
    • Delivered a toolbox and recommendations for potential suppliers and partners to enhance the IMS:
      • Several leading data sets providing broad access to available business data in Norway and Europe.
      • Leading ontologies and vocabularies for harmonizing and enriching data with machine-learning.
      • Advanced methods and software for data mining, harmonization, and enrichment.

    Project Outcomes

    Clear path for development of advanced Big Data solutions (software, methods and data).

    • Data Harmonization/Fusion: integrate multiple data sources to dynamically produce structured data.
    • Data Enrichment: develop HITL machine-learning models to dynamically enrich missing/incomplete data.
    • Data Exchange: re-use “core” business data securely and cost-effectively across platforms and users.

    Scalable frameworks and templates for further research and studies into regional dynamics.

    Plan and partnerships to build a user ecosystem and test platform for data exchange.

    Clear project opportunities and expressions of interest from leading research institutions.

    • SINTEF invitation to join the EU Horizon “EnrichMyData” industrial research project with OECD.
    • Dialog with SINTEF and NTNU-NorCIS to establish NRC Collaboration Project in cybersecurity.
    • Private sector consortium of digital platforms in NRC Innovation Project (IPN) for data exchange.
    • Assisting INN to establish “Inn-the-Loop” NRC Social Impact Project (KSP) in artificial intelligence.
    • Dialog with INN and IESE Business School to apply for EU Horizon Expanding Investments Ecosystems.

    Want to learn more?

    Contact VentureNet for more information about the project results and collaboration opportunities.

    VentureNet

en_USEnglish
nb_NONorwegian en_USEnglish