FAME Technological Framework

Conceptual View

Considering the current status of Data Spaces and their cross-cutting challenges, Data Spaces’ stakeholders are not able to easily access, manage, share, trade, or exchange data assets of their interest. Most of the times, they must follow complicated, unstructured, and energy-hungry roadmaps towards extracting useful information, usually leading to dead ends, increased levels of complexity, and “spaghetti junctions”, in which even simple functionalities such as data rights management mechanisms, must vary for each platform and interacting party. What can be also seen as a blocking issue are the bottlenecks of identifying fairly priced, interlinked, and even correlated data assets, often related to different tools, models, services, and applications that have been constructed and implemented from diverse user groups with different levels of technical knowledge and design requirements. Even in the case that such tasks are successfully accomplished, most of the times this leads to complex, energy-harvesting, and time-consuming processes requiring multi-aspect data engineering efforts and skillset. To go one step beyond this problem, FAME’s vision is to act as a single-entry point of low complexity, offering the toolsets to gain valuable insights in the EmFi application domain, by exploiting cutting-edge technologies (e.g., AI, ML, blockchain).

Thus, FAME is a joint effort of high expertise in data management, data technologies, data analytics, data economy and digital finance to develop, deploy and launch to the global market a unique, trustworthy, energy efficient, and secure Federated Data Space for EmFi application domain. The core Technological Framework of FAME is the Data Marketplace that heavily relies on its developed Federated Data Assets Catalogue (FDAC). In this direction, FAME enhances state-of-the-art Data Spaces’ infrastructures by constructing a Federated Data Marketplace that supports decentralized and programmable data assets’ trading and monetization, offering at the same time the capability to apply trustworthy and energy efficient analytics upon those assets. Towards this direction, FAME provides novel functionalities in three (3) core directions: (i) Secure, interoperable and regulatory compliant data exchange across multiple federated data providers in-line with emerging EU initiatives, (ii) Decentralized and programmable data assets’ trading and pricing leveraging blockchain tokenization techniques, and (iii) Integration of trusted and energy efficient analytics based on innovative technologies such as XAI to gain higher trust in data analytics, Situation Aware Explainability (SAX) to offer sound and contextually enriched explainability of data analytics, incremental energy efficient analytics, as well as power-efficient edge analytics.

Below, a conceptual view of the FAME Data Marketplace is provided. specifying how both data consumers, as well as data providers can benefit from such solution. In deeper detail, following the top-down approach, any data consumer (e.g., SMEs, Data Scientists, Research Centre, Financial Organizations) who wants to access the FAME provided features can be authenticated and authorized access using the appropriate components, being then able to search and identify the desired data asset for further usage. On the other side, following the bottom-up approach, it can be identified the pathway that can be followed from any data provider (e.g., Data Space, Data Marketplace, Database owner, etc.), to connect to FAME and index the data assets to be provided, for further exploitation. In both flows, all the technical and business values as well as the objectives of FAME are considered (e.g., data sovereignty, blockchain tokenization, federated data access, energy efficient analytics).

Technical Capabilities

The FAME Federated Data Marketplace encompasses several innovative tools and mechanisms that provide specific capabilities, all in one realizing the vision of a unique, trustworthy, energy-efficient, and secure federated EmFi Data Marketplace that offers novel decentralized programmable pricing and trading of data, among others. Hence, the FAME’s capabilities are related with the opportunities of typical Data Spaces and Data Marketplaces, additionally offering: (i) an one-stop shop federated data catalogue integrating and linking data assets from external Data Marketplaces and applications, (ii) a single-entry point authorization and authentication infrastructure, (iii) a decentralized, configurable, dynamic, value-based data assets’ trading and monetization capability, and (iv) a set of tools for applying trusted and energy-efficient analytics. All the underlying capabilities are presented below.

1. Operational Governance

   By providing an extensive range of administration and governance functions necessary for the effective operation of the federation, the Operational Governance (GOV in brief) module considerably increases the FAME federation’s overall functionality. End-users can benefit significantly from these additional capabilities, which enhances their experience within the federation.

   The GOV module, first and foremost, facilitates the accession of new members and the withdrawal of existing members from the federation, streamlining the procedures for both. This guarantees simple and trouble-free maintenance of memberships. Without complicated processes, new members can join right away, and departing members can do so without facing any red tape. The federation’s ability to smoothly transition members ensures that it will always be dynamic and flexible, able to meet the changing needs of its members.

   The module also has a strong support system that improves user experience by quickly and effectively handling user questions and help tickets. The prompt resolution of customer complaints by this committed support team enhances user pleasure and builds platform confidence. Quick support is available to help prevent problems before they get worse, which builds user confidence and dependability.

   Moreover, the GOV module enhances thorough and safe federation transactions. The capacity to handle transaction accounts, interact with other trading parties in an efficient manner, and obtain informative and historical data is advantageous to end-users. This improves their trading ability and all-around strategic planning by providing them with the tools they need to make wise selections. Additionally, the safe transaction features guarantee that all financial activities are protected, instilling confidence in the safety of their operations.

   Overall, the FAME federation is more effective, safe, and intuitive thanks to the GOV module. It guarantees solid commercial operations, effective user administration, simple integration processes, and adaptable support services. The GOV module’s provision of these all-inclusive management and governance functionalities enhances the end-user experience, while streamlining administrative processes, so rendering the FAME federation a more appealing and efficient milieu for all its constituents.

2. Authorization & Authentication

   Authentication and authorization are two (2) fundamental security processes in the management of access to systems and resources. In the context of FAME, these processes are extremely crucial in protecting sensitive data and user privacy, and in maintaining the overall system’s integrity.

   More specifically, authentication is the process of FAME for verifying the identity of an external user or system. Its primary function is to ensure that the entity requesting access is who or what it professes to be. Such functionality is of high importance for the FAME stakeholders as well, since authentication will help them to ensure that only verified users have access to their accounts, thus protecting their personal and sensitive data from unauthorized access. At the same time, once FAME identifies the user through authentication, it is able to deliver a personalized user experience, including preferences, settings, and other personalized data.

   Once authentication has been achieved, the next step is the authorization. In the context of FAME, authorization grants or denies access to specific resources or permissions within the developed Data Marketplace once a user’s identity has been authenticated. Such functionality is of high importance for the end-users as well, since authorization allows the control of access to sensitive information on a need-to-know basis, restricting what users can see and do within the Data Marketplace based on their individual roles or privileges. At the same time, the supported authorization process limits the access to information based on the user’s roles, reducing the risk of accidental changes or unauthorized data exposure.

   Hence, through authentication, the FAME stakeholders are assured that their accounts and data are protected from unauthorized access, whilst through authorization, they always know that their actions within the system are appropriate to their role, being protected from accidental misuse or deliberate data breaches. To successfully support such concept, the FAME Authentication and Authorization Infrastructure provides Self Sovereign Identity (SSI) capabilities, based on Distributed Identity and Verifiable Credentials, maintaining the most used authentication and authorization flows and standards in this moment, to facilitate the integration of stakeholders’ applications and incentivize a wide adoption. To be more specific, the SSI concept has been chosen to be adopted for solving the following issues: (i) Identity and personal data are stored with the user, (ii) Claims and attestations can be issued and verified among users and trusted parties, (iii) Users selectively permission access to data, and (iv) Data only needs to be verified a single time. As for the Blockchain technology, providing decentralization, immutability, and cryptographic security, it allows the creation of credentials that can be issued and verified without the need of a central certification authority and can be owned by the end-users and directly shared with third parties without involving the credential issuer.

3. Federation of External Sources

   The ability to discover and index data assets outside of the FAME platform is essential to allow FAME to perform discovery, exchange, pricing, and trading of relevant assets published in external Data Spaces/Data Marketplaces. In FAME, the Federated Data Assets Catalogue (FDAC) provides such functionality through a developed mechanism to describe and execute asset metadata importers - called Resolvers. The latter are able to connect to the associated source of assets, use the available interfaces to explore the available assets, read the metadata of the assets described on the source’s data model, extract the information, and add it to the FDAC. Among the information extracted by the Resolver, this refers to the description of the asset, the location of the asset, any existing pricing information and knowledge about any policy related with the assets discoverability and purchase conditions.

   Since different sources of assets may expose different APIs and represent information using different data formats, multiple Resolvers may need to be implemented. However, the assets’ sources may follow Reference Standards or Architectures that define the interfaces and the data models used for communication, contributing to reduce the amount of heterogeneity among diverse sources. Therefore, a Resolver implemented to follow one of these References will be able to be reused to fetch information from several sources that also comply with the Reference.

   This characteristic also assumes relevance in the European ecosystem where RAs, like IDS and Gaia-X, emerged and are getting popularity. To this context, a Resolver implemented to comply with the interfaces and the data models of one of those specifications will be able to be reused to connect to the multiple Data Spaces/Data Marketplaces that also implement it.

4. Assets Policy Management

   The Assets Policy Management plays a crucial role in ensuring the secure management of data assets within FAME. This component serves two (2) key functionalities, both of which are vital for the smooth operation of FAME Data Marketplace.

   Firstly, the Assets Policy Management enables the complete lifecycle management of policies associated with the federated data assets that are discoverable through FAME, determining who is eligible to view and potentially acquire specific assets within FAME. Leveraging the Attribute Rule-Based Access Control (RuBAC) model, the component considers various user and organizational attributes to combine them in boolean expression formatted rules to make informed policy decisions. By fulfilling its role as a Policy Decision Point (PDP), the Assets Policy Management ensures that other components of the platform always display the appropriate data assets to authenticated and authorized individuals or organizations. In essence, it acts as a central authority, facilitating the enforcement of access controls throughout the platform.

   Secondly, the component provides end-users with a comprehensive list of the data assets they own. This encompasses assets uploaded by the end-users themselves or any other member of their organization, as well as assets acquired by them and have active contracts. This functionality offers end-users a clear and consolidated view of their assets, enhancing their ability to manage and track their assets portfolio effectively. This functionality also enhances transparency and accountability within the platform, allowing end-users to have a clear view of the assets under their control.

   Overall, the Assets Policy Management plays a critical role within FAME, since it serves as the central authority for managing data asset security policies and ensuring that the appropriate data assets are displayed throughout the system. By acting as a PDP, it enables effective access control enforcement by other components, whilst it provides to the end-users a comprehensive overview of their owned data assets, enabling better asset management and control. With its pivotal functionalities, the Assets Policy Management reinforces the FAME’s security posture, fosters transparency, and promotes efficient assets’ management practices.

5. Assets Provenance & Tracing

   The trustworthiness of any Data Space depends on several conditions, a key one being the availability of the metadata that the end-users can rely on. This means three (3) things: firstly, providing metadata attributes that unambiguously identify the nature, meaning the provenance of the underlying data asset; secondly, managing additional attributes that add market-related information to the asset (e.g., terms & conditions and pricing); lastly, ensuring the authenticity and integrity of such attributes, so that their content cannot be tampered by malicious actors. The Assets Provenance & Tracing component provides these capabilities in the context of FAME. In essence this component is the implementation of a registry where the identities of verified sources and the digital fingerprint of catalogue entries are stored as permanent and immutable records, so that any inauthentic version of these key information items (e.g., a counterfeit entry from the catalogue of a federated data space) can be easily spotted. Being blockchain-based, the component’s registry is shared by all the members of a federation.

   From the perspective of a publisher (i.e., a user sharing a data asset), the integration of the Assets Provenance & Tracing component in a federated Data Space gives a high level of confidence that what has been provided as the description of the published item cannot be altered (e.g., by a malicious actor posing as the publisher). From the perspective of a would-be consumer, it adds trustworthiness to the FDAC, as the provenance of the asset and the integrity of its catalogue entry can be relied on.

6. Assets Pricing

   FAME is implementing a data-driven pricing advisory mechanism leveraging issuer-provided data, stakeholder survey responses, and historical pricing realization analytics. The Assets Pricing component plays an important role in the development of the business side of FAME. This component consists of two (2) key functionalities, both of which are quite important for the transactional operation of the FAME Data Marketplace.

   Firstly, it suggests an objective value (i.e., price recommendation) for different data assets and services leveraging the metadata of the data assets. To this context, it specifies and implements a set of different pricing schemes for different data assets, using metadata information, including the asset’s completeness, volume, quality, timeliness, CO2 wastes, user friendliness, trustworthiness, and more. This solution is based on a questionnaire filled by the seller based on the characteristics of the various data assets, whereas to implement the above listed schemes, this component leverages the Assets Provenance & Traceability API of the blockchain infrastructure. Secondly, the proposed price also reflects the results from the analysis of the transaction price of similar data assets identified by the Similarity Analysis.

   Overall, all the weighting factors and values obtained through the questions from the sellers are important for asset valuation. These help to ensure the subjective aspect of valuation, as the primary goal of valuing individual assets is to achieve satisfaction, especially from the seller’s perspective. In addition, the seller can choose the best price that would reflect all costs associated with acquiring the provided data asset. Hence, the result of this component is a price recommendation for the end-users (sellers), being targeted to help them better determine the price of the asset they are offering. The sellers can either lean towards the recommended price or set their own price, supporting a seller-oriented approach.

7. Assets Trading & Monetization

   The Assets Trading & Monetization component is a key component of FAME, designed to facilitate the secure and efficient trading of data assets within the offered Data Marketplace. It leverages the power of smart contracts to bring significant value to both providers and consumers of data assets.

   For data asset providers, the Assets Trading & Monetization component offers a robust and secure mechanism for monetizing their data assets. By utilizing smart contracts, it ensures that providers are fairly compensated with ERC-20 tokens for the value they bring to the Data Marketplace. This not only provides an immediate revenue stream for providers but also incentivizes the continual addition of high-quality data assets to the platform. Different smart contract transaction models (pay-as-you-go, subscription, pay-as-you-use) are implemented to allow payment schemes that fit best the nature of the data asset.

   For data asset consumers, the component simplifies the acquisition process. Consumers are able to easily browse the FDAC, identify data assets of interest, and acquire them through a transparent and secure trading process. The use of ERC-1155 tokens in this process allows for the accrual of value in the case of certain data assets, enhancing the potential return on investment for consumers.

   The Assets Trading & Monetization component also plays a crucial role in maintaining the integrity and transparency of the Data Marketplace. All the trade details are logged on a ledger, providing a clear audit trail that promotes trust among end-users and ensures compliance with regulatory requirements. In essence, it streamlines the trading process, ensures fair compensation for providers, simplifies asset acquisition for consumers, and fosters a transparent and trustworthy marketplace.

8. Assets Searching

   The semantic search engine provides a unified search interface fully integrated in the FAME Dashboard enabling real-time data availability, and enhanced discoverability. In that sense the end-users are able to quickly find relevant data assets according to their needs/requirements, for analysis and decision-making. Thanks to the single-entry point for finding data assets’ information through the developed searching mechanism, FAME not only ensures data security and governance to its end-users, but it also promotes collaboration and knowledge sharing while optimizing resource efficiency.

   The semantic search engine has access not only to the FDAC but it also sends the user request to other Data Marketplaces supporting the federation approach of the project like European Data and Google Dataset Search, among others. In addition to that, the semantic search engine offers the requested data assets ranked not only considering semantic criteria, but also considering additional criteria such as the assets’ price, size, and format. Lastly, this service also suggests related searches to offer the end-users complementary options based on semantic analysis of the principal search query. Hence, the main goal is to provide the data asset consumers the most relevant offer depending on their specific needs and requirements for data exploitation.

9. ML & AI Analytics

   The ML & AI Analytics bring to FAME the capability to understand EmFi applications related problems and get the necessary insights to automate decisions based on the input data that the needs that the clients have. Realizing a catalogue of available ML techniques is the main goal of this component. Moreover, functionalities for training these algorithms, as well as the capability to infer output given new data samples, is also implemented. State-of-the-art models such as Transformer, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN) or classical ML models are provided to address different EmFi applications’ tasks, which comprise solving time series, sentiment analysis, ranking system, or recommendation system problems.

   Hence, the ML & AI Analytics catalogue provides the FAME end-users with tools for understanding either their own data, or the data provided by existing external ecosystems (e.g., Data Marketplaces, Data Spaces), in an intuitive manner without the need to have a deep understanding of ML techniques, allowing them to take decisions according to the model’s outputs. What is worth mentioning, is that all these models can be accessed in two (2) different ways: (i) Analytics as a Service, where the end-user is provided with an endpoint to a third-party server, where he/she can query his/her data. This option is the most suitable one when the end-user does not have neither any ML-related knowledge, nor the necessary computing capabilities to deploy the AI/ML assets; (ii) Analytics as an Asset, where the end-user downloads the analytics he/she needs and runs them on his/her own premises. This option is the most suitable one when the end-user does not have ML expertise, but he/she has the DevOps/infrastructure needed to execute the asset.

10. SAX & XAI Analytics

    The main functionalities of the FAME developed SAX capability revolves around process-aware explainability services, aimed at providing comprehensive insights into analysed processes given their event logs as input. These capabilities include the discovery of causal execution dependency views, allowing the end-users to understand cause-and-effect relationships among events and decisions. SAX also enriches the event logs with contextual information, focusing on significant situations to gain a deeper understanding of the processes. The component offers explanations, both local and global for a set of instances, with features like deriving process outcome relevant attributes ranked by importance, streamlining attributes inferred through causal execution dependencies, and filtering/augmenting contextual-related attributes. Additionally, SAX synthesizes and streamlines explanations, ensuring they are sound and interpretable, empowering end-users to make informed decisions and gain a clear understanding of the underlying processes. The SAX analytics is materialized via a set of services in the SAX4BPM library to be released as open source by the end of the project.

    It is important to note that the SAX/XAI techniques are tightly coupled with the ML models. Therefore, the SAX4BPM library can be exposed in the FAME platform (alongside the ML models) and consumed in the two (2) following ways, as in the case of the ML & AI Analytics: (i) Analytics as a Service, where the end-user is provided with an endpoint to a third-party server, where he/she can run the analytics on his/her data. This mode of consumption requires the monetization of “service” assets in the FAME platform; (ii) Analytics as an Asset, where the end-user downloads the analytics he/she needs and runs them on his/her own premises. Being one of the data assets indexed within the FDAC, this mode is subject to FAME’s Data Marketplace manipulation on assets.

    As for the XAI Scoring framework, it provides an additional capability for scoring the explainability of the different models towards comparing alternative approaches. The provided explanations are easily interpretable by human-users who can also express their preferences over the explanations based on the considered attributes. In essence, the XAI Scoring framework serves as a transformative tool that evaluates the explainability of the produced models, offering to end-users the benefits of: (i) Trust gauge, since by understanding AI decision-making processes, allows end-users to better trust and rely on the system’s outputs, (ii) Human-focused user experience, since the framework prioritizes the user experience, ensuring that AI is not just technically sound but also user-friendly, and (iii) Benchmarking, enabling users to compare different AI techniques, making informed decisions about which models best align with their objectives. Additionally, the output of this framework aids in refining the pricing of the AI data assets provided/produced into FAME, ensuring that valuations are both accurate and reflective of their true explainability and utility. By using those components, the end-users are able to understand what makes a model to perform the way it does or identify which data that they are feeding with the model is more valuable in the given estimations.

    Moreover, XAI Scoring framework is developed in a way that it is use case agnostic in terms of usability, but it takes into consideration the underlying sector or industries, such finance, where clear model explainability is crucial for regulatory compliance and stakeholder trust. This practical utility highlights the framework’s potential to enhance decision-making processes across different sectors in the future, underscoring its significant impact.

11. Incremental & Energy Efficient Analytics

    The purpose of this component is twofold: firstly, to provide the capability of incremental processing of analytical operations, and secondly to perform analytical query processing with an energy efficient manner.

    Regarding the incremental analytics part of this artefact, it is important to highlight that with this term we refer to the analytical query operations that can be found in a relational database. These can be used by AI analytical processing in order to push such operations down as closest to the storage. The benefit for the end-user, the data analyst or application developer, is that they do not have to bother on how to execute such complex operations, but instead, leave the database to perform those on their behalf. By doing so, the data analyst or application developers do not need to read enormous volumes of data, transmit them to the application or analytic layer and do the process there, but retrieve calculated results to be later used. This reduces significantly the resources needed for such operations, along with the observed latency, thus being as much closer as possible to offer near real time analytics. However, the analytical operations include an inherit complexity. In order for any data management system to calculate the results, this implies the scanning (or access) of a plethora of data elements that reside in a big dataset. Each invocation of such operation is time consuming and demands the consumption of significant resources, even if they are performed close to the storage itself. Given this innovative characteristic, the Incremental & Energy Efficient Analytics component can calculate these results incrementally, that is, as data modification operations occur in parallel. This means that the end-user can observe minimum latency, as the result is pre-calculated and modified incrementally. This can happen while database transactional semantics are ensured on the other side, thus, having the results being consistent in case of parallel modifications of the data. This allows for the provision of real-time analytics (as latency is minimized) while data can be concurrently ingested at the same time (as database transactions are enforced).

    The second innovation of this component is its ability of performing advanced query processing in an energy efficient fashion. Firstly, by exploiting the incremental analytics part, it does not have to calculate every time the analytic result. This results in reduced energy consumption. Furthermore, based on the novel indexing mechanism and the implementation of the query engine, the module can offer reduced energy consumption by managing data in a more sophisticated manner. End-users can take advantage of this feature while designing their applications or AI data pipelines as their overall solution can result in reduced carbon footprint compared to the greedy consumption of resources that complex AI analytic often require.

12. Smart Deployment

    The Smart Deployment component provides end-users with deployment templates for analytics components so that they can be deployed standalone or in pre-defined pipelines. This allows FAME end-users to run complex analytics services without requiring in-depth knowledge of the various components that compose the workflow.

    These pipelines are monitored for CO2 emissions and optimised to promote the reduction of these emissions. This means that the workflow of a complex service evolves according to the metrics collected by the CO2 Monitoring component, with the objective of minimizing it.

13. Analytics CO2 Monitoring

    Estimating the CPU usage of the AI/ML models used within the end-users’ applications exploiting FAME is the main functionality of the Analytics CO2 Monitoring component. This metric can then be translated into the energy consumed by the model. Using public information or public databases on the average kilograms of CO2 emitted per kilowatt-hour per nation, this component then optimises the use of the available models within FAME in terms of consumption.

    As far as it concerns the end-users, this functionality does not have a direct impact on them, as it is aimed at monitoring and reducing CO2 emissions, hence the main impact is to raise awareness of the end-users. However, as an optional functionality, it is intended to show end-users in a graphical way the consumption of the models they use through appropriate visualizations and charts.

14. FML Deployment

    When data is collected in a massively distributed manner, with many sensors geographically distributed, there may be circumstances in which gathering all the data in a central server is not desired, mostly due to two (2) reasons: (i) data privacy must be preserved, either to protect users’ anonymity, intellectual property, or both, and (ii) sensors often have limited communication resources, for example, if they are battery-powered or they are connected to low data rate networks (e.g., see the Internet of Things).

    However, despite the distributed and private nature of this data, there still are incentives to build ML analytics based on it, since correlation is likely to exist across the different datasets. Therefore, if a common model could be trained without sharing the data, all parties could benefit from better models rather than individually training models on the end devices.

    Federated Learning (FL) provides the mechanisms to train aggregated models from clients collecting data in a decentralized manner. Instead of transmitting the raw data to a central server, the clients send the model updates obtained with the local data. As a result, FL achieves higher data privacy, since the raw data is maintained in the clients, and improves energy efficiency, since only the updates must be transmitted. The transmitted model updates are protected with differential privacy, which ensures the privacy of the raw data and minimizes any data leakage.

    FAME’s FML Deployment implements the required components to provide such a privacy-friendly, energy-efficient, and decentralized model training mechanism, where end-users can locally gather data and train their models in the edge, whilst the aggregated model is generated at the central server deployed in the cloud. Every training round finishes once the central server distributes the aggregated model to the clients. Following this notion, the FML deployment allows end-users to access FL Analytics that enable them to make decisions based on their own data without any FL-specific knowledge. Moreover, FL Analytics as a Service is also offered, such that the end-users can cooperate with third parties in the data ecosystem built in FAME, while preserving the privacy of their own data.

15. Learning Centre

    FAME offers to its end-users with access to a pool of training assets’ resources, notably resources related with EmFi applications. To this context, the EmFi Training implemented functionalities of FAME enable its end-users to: (i) Search and access training resources, including courses, webinars, tutorials, how-to videos, and FAME-related demonstrators, (ii) Search and access knowledge resources, including research papers, whitepapers, blog posts and other knowledge-related content items, (iii) Search and access the contents of the training resources (e.g., courses) catalogue, (iv) Integrate new EmFi training resources, and (v) Integrate with the FDAC to ensure that the resources of the Learning Centre are accessed as FAME data assets and are part of the FAME Data Marketplace, effectively yielding the Learning Centre as one more platform that will be federated around FAME. Some of the above-listed will be accessible only to properly authorized end-users, while others will be made available to all the end-users of FAME.

16. Regulatory Compliance

    Recently, various advanced Data Marketplaces have been developed in Europe, providing functionalities for data catalogues, search, analytics, trading, and accounting. Marketplaces such as i3-MARKET, DataVaults, MOSAICrOWN, MUSKETEER fall within these implementations, providing added-value features for integrating, accessing, and trading data assets, such as data assets monetization, data sovereignty, personal data protection, compliance to regulations (e.g., to GDPR (General Data Protection Regulation)). As stated above, FAME’s ambition is to deliver Europe’s first standards-based, secure, regulatory compliant, interoperable, and federated Data Marketplace for EmFi applications. Apart from the unique feature of the FAME Data Marketplace of the federated access control, FAME also provides a unified access to all related regulations in the field. In that sense, it provides a harmonious ecosystem of the laws and regulations according to the need of the different interested stakeholders.

    The FAME Regulatory Compliance tool is based on the definition and enforcement of policies that ensure compliance with applicable laws and regulations such as GDPR, PSD2 (2nd Payment Services Directive), and 4AML (4th AntiMoney Laundering directive). Hence, the main objective of this tool is to ensure compliance of the FAME functionalities with related laws and regulations that is to comply with the security and regulatory requirements deriving from EmFi applications. In doing so, the Regulatory Compliance tool is set according to the prominent regulations of the sector (i.e., PSD2, MiFIDII, 4AML) in addition to general regulations (e.g., GDPR AI Act). Therefore, the ultimate regulatory compliance asset is a tool applicable for unified data policies management in-line with security-by-design and regulatory compliance by design principles.

17. Dashboard

    The main reason behind the Dashboard of FAME is to provide to all of its end-users (both technical and non-technical stakeholders), a user-friendly end-to-end UI that will facilitate their interaction with all the involved FAME services, components, and processes, communicating with them via their Open APIs. In particular, a dashboard has already been designed, specified, and implemented in such a way that it enhances user experience, driving user engagement, and ultimately determining the success of FAME. It focuses on providing an interface that is aesthetically pleasing, simple to use, inviting all the FAME stakeholders (either end-users or platform administrators) to further explore all the associated capabilities. As a result, user experience is impacted, with an emphasis on streamlining complicated FAME-related activities (such as searching, monetization, and trading), guaranteeing fluid navigation, and ensuring that end-users can perform their tasks with ease.

    Consequently, the Dashboard’s primary goal is to maintain consumers’ interest throughout time. To successfully achieve such planning, the implemented Dashboard has visually appealing components, engaging tools, and considerate animations to produce a captivating and enjoyable user experience. As a result, the interacting stakeholders are more likely to use FAME capabilities, discover its features, and spread their positive experiences, which will promote in general FAME acceptance and growth. To ensure that all the stakeholders can connect effectively and to make FAME accessible to a wider audience, it also considers accessibility elements including appropriate colour contrast, font sizes, and sensible navigation options. This in turn improves its usefulness and reach.

    Considering all these features, a strong brand identity will be built, helping towards transforming FAME to a widely known single-entry point of data assets and functionalities related with EmFi applications. FAME’s value will be reinforced, establishing a strong connection with all the interacting end-users, prioritizing both technical and non-technical stakeholders, thus creating a consistent experience across diverse related Data Spaces and Data Marketplaces.