Distributed data visualisation

In short:

  • In the data space, AI providers will be able to interact with data providers in order to process their datasets and generate results (recommendations, analytics, predictions, etc).
  • These results need to be easily integrated into different apps and UIs.
  • Usually the output of a system is fed back to the system who provided the input. Our distributed approach will enable the output to be sent and shown to any other participant of the data space.


Start date: T0 (expected: Q1 2024)
End date : T0 + 12 months
Duration (in months): 12

Where we are right now

  • Developments have yet to start
Want to join the effort? See the Working Groups.


An example:
  1. 1.
    A person has skills data in a skills portfolio
  2. 2.
    they are looking to see what kind of job would be fitting for them
  3. 3.
    a service allows them to have their data processed by multiple AIs and compare the results
  4. 4.
    this service, instead of having to integrate a connector with the skills portfolio and with each of the AIs, only connects with the data space connector
  5. 5.
    all that can be analyzed on the edge, will be analyzed on the edge. What can not will be tokenized to ensure maximum privacy and personal data protection.
  6. 6.
    the service developer can design their ecosystem where the person can give their consent for their skills data to be accessed by the AIs from the skills portfolio and the results appear in the service’s UI
This means:
  1. 1.
    the data providers and AI providers have a way of identifying where to send the results
  2. 2.
    the UI provider has a single plugin to show results from any source


The idea is to reuse widely used assets, industry standards and standards that are already in use, eg. following:
  • standard data formats, like JSON-LD and xAPI
  • standard data visualization models like tables and graphs
  • open source frameworks, like D3.js and Chartist.js (standard way to use)
  • data storing technologies, like browser-cookies (industry standard) and data-api:s (standard way to use)

Roles between partners:

Headai: Use case definitions, Data model, visualization component,
Institut Mines Telecom: Use case definitions, Data model, compatibility with other tools and systems
Visions: Use case definitions, compatibility with other tools and systems, integration of component in VisionsTrust data intermediary and with other functionalities (consent, contract)