Periodic Reporting for period 1 - DS2 (DataSpace, DataShare 2.0)
Reporting period: 2024-01-01 to 2025-06-30
Data, data economy and sharing data have been recognised as a key topic in the digitalization of the society. Capability to create, process, and use data across organisational boundaries with losing the data sovereignty, i.e. the rights of the data owner to control the use of its data, is the key enabler of data-driven systems. Data spaces have been proposed as a technology to share valuable and confidential data between companies. Data spaces combine the secure transfer of data with legally binding contracts that define how data can be used and what data rights are transferred as a part of the data transaction. The idea of the data space is to break data silos between the companies that are committed members of the data space, but because of the needed trust mechanisms, the data space creates a silo itself.
The overall objective of DS2 is to develop a data space interoperability solution that enables data space members to share data using data space technology with members of another data space, when these two data spaces have agreed to do so. The interoperability solution needs to support the data sovereignty and governance through the complex life cycle of data. Because data spaces and DS2 are novel ways for a company/organisation to get and share data with third parties, it needs to be integrated with their data management systems. The DS2 will also tackle the problems with managing and governing these aspects, and with supporting process where human involvement is needed with advanced AI/LLM technologies.
Data space interoperability is needed in common European data spaces that are essentially sectoral interoperability solutions with data spaces as their members. Data sharing enabled by them and DS2 solution can have significant impacts in enabling the AI/LLM revolution in industrial and commercial settings, where data is not openly available. Development, maintenance and use of AI is fully dependent on the data, and DS2 provides means to add confidential data as an element in these processes. More traditional use cases such as supply network optimisation, asset management, various cyber-physical system, and smart services will benefit from data that is not otherwise accessible. The expected economic impact of data economy is hundreds of billions of Euros.
The overall objective of DS2 is to develop a data space interoperability solution that enables data space members to share data using data space technology with members of another data space, when these two data spaces have agreed to do so. The interoperability solution needs to support the data sovereignty and governance through the complex life cycle of data. Because data spaces and DS2 are novel ways for a company/organisation to get and share data with third parties, it needs to be integrated with their data management systems. The DS2 will also tackle the problems with managing and governing these aspects, and with supporting process where human involvement is needed with advanced AI/LLM technologies.
Data space interoperability is needed in common European data spaces that are essentially sectoral interoperability solutions with data spaces as their members. Data sharing enabled by them and DS2 solution can have significant impacts in enabling the AI/LLM revolution in industrial and commercial settings, where data is not openly available. Development, maintenance and use of AI is fully dependent on the data, and DS2 provides means to add confidential data as an element in these processes. More traditional use cases such as supply network optimisation, asset management, various cyber-physical system, and smart services will benefit from data that is not otherwise accessible. The expected economic impact of data economy is hundreds of billions of Euros.
During the first half (M1-M18) of the project the work has consisted of
· definition of data space interoperability problems space,
· the analysis of trust creation and trust enforcement processes,
· analysis of regulatory landscape of data spaces,
· data product and data sharing agreement concepts,
· specification of the three pilot use cases and defining the use cases setups,
· creation of DS2 architecture model, including ideas for deployment, interoperability, and data pipeline systems,
· specification of more than 20 DS2 modules including research related to how the initial ideas of the module functionalities could be implemented,
· implementation of first module prototypes, and
· initial analysis of business model and sustainability of data spaces and interoperability operator.
The main achievements at M18 are
· deep understanding of the various alternatives and their future directions of data spaces, and a scientific paper about the core features of data spaces,
· conference paper on the relationship between European regulation and data spaces’ innovation potential,
· user survey on end-users’ attitudes related to data sharing and data spaces,
· data space interoperability architecture model,
· DS2 module documentation in DS2 GitHub that provides the basic information of all developed modules.
· definition of data space interoperability problems space,
· the analysis of trust creation and trust enforcement processes,
· analysis of regulatory landscape of data spaces,
· data product and data sharing agreement concepts,
· specification of the three pilot use cases and defining the use cases setups,
· creation of DS2 architecture model, including ideas for deployment, interoperability, and data pipeline systems,
· specification of more than 20 DS2 modules including research related to how the initial ideas of the module functionalities could be implemented,
· implementation of first module prototypes, and
· initial analysis of business model and sustainability of data spaces and interoperability operator.
The main achievements at M18 are
· deep understanding of the various alternatives and their future directions of data spaces, and a scientific paper about the core features of data spaces,
· conference paper on the relationship between European regulation and data spaces’ innovation potential,
· user survey on end-users’ attitudes related to data sharing and data spaces,
· data space interoperability architecture model,
· DS2 module documentation in DS2 GitHub that provides the basic information of all developed modules.
Currently no data space interoperability solutions exist that supports different types of data space implementations. The DS2 solution is unique. The solution consists of federation services (cross-data space identity management, DS2 catalogue, logging service, and interoperability governance services) and participant services that are based in data space protocol currently being standardised by CEN-CENELEC.
Several DS2 modules contain functionality that goes beyond current state of the art.
· Natural language assistant for configuration of data processing pipelines and DS2 functionalities for supporting user’s data management
· Data analysis and modelling method for supporting and improving the queries to meta-data catalogue and LLM services through improved understanding of the context and language independency
· Natural language assistant for data product search from data product catalogue
· Natural language service for accessing complex data from data sources
Several DS2 modules contain functionality that goes beyond current state of the art.
· Natural language assistant for configuration of data processing pipelines and DS2 functionalities for supporting user’s data management
· Data analysis and modelling method for supporting and improving the queries to meta-data catalogue and LLM services through improved understanding of the context and language independency
· Natural language assistant for data product search from data product catalogue
· Natural language service for accessing complex data from data sources