Prometheus-X Components & Services

Data value chain tracker BB – Design Document

The Data Value Chain Tracker (hereafter DVCT) is a service that monitors and distributes digital incentives for data usage. DVCT ensures traceability and accountability of data usage, as well as enables organizations and individuals to realize the value of their data.

As a data provider or data owner, DVCT allows providers to determine in which use cases, when, how, and by whom the data was used, what other data was merged together and delivered to the end user.

Data providers (individuals or organizations) get an overview of where their data is used and can obtain information about the value of their data in the ecosystem, this can help them to better negotiate their data and service offering. In addition, by tracking the use of data in the ecosystem, the DVCT will handle the distribution of digital incentive to the organizations that participate in the value co-creation of the data usage.

Conceptual Overview

According to Latif et al. (2009), there are three different types of data that contribute to the creation of the value chain. These are raw data, linked or chain data and consumer data (human-readable). By identifying these three types of data, the DVCT can create the chain based on the parent and child nodes.

source: Linked data value chain (Latif et al., 2009)

A node represents the data processing or the activity that takes place from data provision, refinement, aggregation to end user (e.g., visualization). To construct the data usage chain node, the data origin for the data processing must be determined. Each data processing represents a child node that is connected to one or more data sources (parent nodes). A parent node can have several child nodes. As a parent node, a data provider offers raw data, which is then processed by one or more child nodes before being delivered to the data consumer.

---
title: Tracking node chain
---
flowchart TD
%% Nodes
    A("fa:fa-database Raw Data 
    from Provider A (Node 1)")
    B("fa:fa-database Raw Data 
    from Provider B (Node 2)")
    C("fa:fa-gears Chain Data 1 from 
    Service Provider 1 (Node 3)")
    D("fa:fa-gears Chain Data 2 from 
    Service Provider 2 (Node 4)")
    E("fa:fa-gears Chain Data 3 from 
    Service Provider 3 (Node 5)")
    F("... (Node n)")
    G("fa:fa-cart-arrow-down Final Data 
    for Data Consumer (Node n+1)")

%% Edge connections between nodes
    A -- Node 1 is parent node for Node 3 --> C
    A -- Node 1 is parent node for Node 4 --> D
    B -- Node 2 is parent node for Node 4 --> D
    C -- Node 3 is parent node for Node 5 --> E
    D -- Node 4 is parent node for Node 5 --> E
    E -- Node 5 is parent node for Node n --> F
    F -- Node n is parent node for Node n+1 --> G
    D -- Node 4 is parent node for Node n+1 --> G

%% Add legend
    subgraph Legend
      direction LR
      start1[ ] --->|Function 1| stop1[ ]
      style start1 height:0px;
      style stop1 height:0px;
      start2[ ] --->|Function 2| stop2[ ]
      style start2 height:0px;
      style stop2 height:0px;
      start3[ ] --->|Function 1 and 2| stop3[ ]
      style start3 height:0px;
      style stop3 height:0px; 
    end

%% Styling the arrow to fit with the legend
    linkStyle 0 stroke:red;
    linkStyle 1 stroke:red;
    linkStyle 2 stroke:Blue;
    linkStyle 3 stroke:red;
    linkStyle 4 stroke:red;
    linkStyle 5 stroke:red;
    linkStyle 6 stroke:red;
    linkStyle 7 stroke:orange;
    %% arrow legend
    linkStyle 8 stroke:red;
    linkStyle 9 stroke:orange;
    linkStyle 10 stroke:Blue;

From the “tracking node chain” image, a use case has for example two analytics functionalities, each of these functionalities requires different Data and AI (Artificial Intelligence) Services. For the function 1, Nodes 3 and 4 are using the data directly from Node 1, while Node 5 and Node n are indirectly using the data from Node 1.

To encourage data sharing, digital incentives should be provided to the ecosystem. These digital incentives can be used to convert the “value” of data sharing into a valuable asset that can be used for various activities within the Prometheus-X (PTX) ecosystem. DVCT act as tool to distribute the digital incentives based on data usage of participants.

For incentives distribution, the prerequisites are:

  1. The number of points (as representative of the data value) to be distributed for data and service providers must be predefined in the contract.
  2. As a default, points will be awarded in full (factor = 1, factor will be between 0.0 - 1.0), unless specified in the contract to check data quality (factorCheck = false). Here, the factor will be calculated with the support of the Data Veracity Assurance building block.
  3. Except for raw data, information regarding previous service data processing (based on a contract) or data origin (from dataspace catalog) must be included.
  4. Incentives should be defined in a catalog, similar to the business model for dataspace offerings, where organizations can specify prices for their offers (additional input fields related to points/tokens for offers and use cases).
  5. Initial points for testing purpose will be generated and given to dataspace participants.
  6. Service or AI Providers should provide log data file regarding usage event, or log file about the dataset usage, training sessions, and performance the models trained with (each) dataset.

Technical Usage Scenarios & Features

Technical usage scenario and role of the DVCT can be described to address the issue of data value ambiguity by giving an overview of data utilization, both direct and indirect:

There are various approaches to incentive mechanisms, including calculating the impact of data on data output (Shapley value, leave-one-out, Banzhaf value, reinforcement learning and Stackelberg game), auctions and contracts (Zeng et al., 2021). However, calculating the impact of data has its limitations: Access to the data is required, data privacy could be compromised, and high resources are required to compute and run the algorithm. This can hinder the early adoption of data spaces and building blocks. Therefore, DVCT will use contracts that do not require access to the shared data and let the market decide the value of the data.

To help Dataspace participants determine the distribution of incentives, the following are some rules that can be used as reference in the contract:

Each data provider has access to a visualization showing detailed reports on their data’s usage and earned rewards.

Moreover, based on the Dataspace Governance Principles defined by IDSA (IDSA applies four core governance principles: Accountability, Transparency, Fairness and Responsibility. Source: International Dataspace (IDSA), Contract should be clearly stated how much incentive points will be distributed among participants. The principles are join work between different BBs, and the DVCT will focus on ensuring transparency and accountability through an immutable database and tracking of data usage.

DVCT does not include rules on how the initial tokens are generated as this is outside the scope of building blocks, the data space that determines how DVCT can acquire tokens. Also, regarding the amount of incentives to be distributed, this is at the use case level, which will be determined by the use case participants in their business model.

Features/Main Functionalities

Based on the DVCT objective and technical usage scenario, there are three key functionalities for the BB, the key functionalities are:

  1. Track the history of direct and indirect data usage of shared data
  2. Handle the distribution of digital incentives (in terms of points, tokens, badges, or labels) based on the contract.
  3. Provide data for visualization of value chain tracking (forward and backward tracking).

Requirements

Some requirements for the DVCT are based on the DVCT objectives, the technical usage scenario, the initial conceptual overview and GAIA-X and IDSA, including*:

Distribution of incentives in DVCT

The objective of the distribution of incentives in the DVCT is to design and implement a system for distributing incentives within a data value chain tracker using blockchain and smart contracts. The orchestrator or other entity will provide the tokens/points based on the contract, and the DVCT system will distribute the tokens/points to providers and consumers.

Incentive for network participants

DVCT will incentivize data providers, AI providers, and contributing consumers based on their contribution to the use case. It will largely operate based on contracts provided by use-case orchestrators. Smart contracts ensure fairness, transparency, and security in the distribution of tokens. The DVCT also hope to see potential for new incentives and business models, exploring novel ways to incentivize and capitalize on existing data flows.

Smart contracts

The smart contracts will facilitate the distribution of tokens. Some of its parameters will be defined from the contract bb, which defines information about a participant’s role, data usage terms, type of data usage and the distribution of points. This ensures consistency and interoperability across the Prometheus-X ecosystem. This needs to monitor the direct and indirect use of data for a given use case to be able to distribute tokens correctly. The mechanisms for payment and incentive distributions are integrated directly into the smart contracts, ensuring fair and transparent compensation for data providers based on the value of their contributions. The DVCT might need to collaborate with some use-case orchestrators to define the contract parameters accurately, considering the specific requirements of each use case.

Blockchain

The DVCT has made the choice to build on an EVM-compatible blockchain. The Ethereum Virtual Machine (EVM) offers the developers significant advantages, especially in terms of familiarity, interoperability, and access to a broad ecosystem. It serves as the backbone of the Ethereum network, where it has been battle-tested, and its compatibility has been widely adopted by numerous other blockchain, creating a standardized environment for decentralized applications. More specifically, the DVCT will be building on Polygon, which is a layer-2 scaling solution for Ethereum. This gives you the security and robustness of Ethereum, whilst enhancing scalability and throughput, and drastically lowering the transaction fees. Since Polygon is fully EVM-compatible you have all the other advantages of building on Ethereum like strong developer tools, ecosystem support, battle-tested contract standards etc. However, since the system is built on an EVM blockchain, it does not suffer a strong lock-in effect, and has the freedom to switch to another EVM-compatible blockchain in the future. This might be needed in the case that another building block decides to use blockchain technologies, need to communicate with the DVCT, and they have some specific requirements leading them to use another blockchain. Communicating between contracts on different blockchains often requires complex bridging solutions and additional layers of coordination. As long as they are on an EVM-compatible blockchain it is possible to coordinate and switch blockchain without much developer cost.

Data visibility

The DVCT system ensures that data visibility is maintained through transparent and auditable processes. Each data transaction and incentive distribution is recorded on the blockchain, providing an immutable and verifiable ledger of all activities. This transparency helps all participants verify the accuracy and fairness of incentive distribution. To minimize transaction fees you should always store as little information as possible in the actual blockchain. The DVCT will just be storing some metadata from the contract that defines how the incentive will be distributed, and the actual records of distribution. This is not considered sensitive data.

Incentive Token

The Incentive Token is an ERC-20 token that will be utilized within the DVCT system to reward participants for their contributions. As an ERC-20 token, it adheres to a widely accepted standard on the Ethereum blockchain, ensuring compatibility with various wallets, exchanges, and decentralized applications.

Responsibilities

The DVCT is responsible for distributing the incentives and storing metadata about how the incentives should be distributed in the blockchain.

Error handling The DVCT is also responsible for handling errors, particularly in the distribution of incentives. The following error handling mechanisms ensure that the incentives are delivered correctly.

Key Features of the Error Handling Algorithm:

This approach ensures that the incentive distribution process is robust, transparent, and able to handle errors efficiently while maintaining fairness and accuracy.

Example scenario: An error is discovered in which an AI service provider receives 10 points less than intended due to an incorrect calculation of its point contribution.

Steps Taken:

  1. Detection: Automated threshold alerts identify the discrepancy immediately after the distribution (compare points on participant side with expected points to receive; compare number of points to distribute with total distributed points).
  2. Rollback: The initial incorrect distribution is rolled back.
  3. Automatic Review: A automatic review by the point based on the contract. If required, manual review should be performed by the building block provider and identifies that the error was due to incorrect data input regarding the miss calculation metrics.
  4. Correction: The metrics are corrected, and the incentive distribution is recalculated.
  5. Re-distribution: The correct points (10 additional points) are allocated to the AI/Service Provider.
  6. Notification: All participants are informed of the error, the cause, and the correction process.
  7. Feedback and Improvement (optional): The process is reviewed, and the rule validation system is updated to prevent similar errors in the future.

By applying these error handling mechanisms, the incentive distribution process ensures that it is fair, transparent, and resilient to discrepancies, ensuring that all participants are appropriately rewarded for their contributions to the data and the data end-result.

Additional points

Token Revocation

A mechanism to revoke tokens from actors who violate contract terms or engage in fraudulent activities. Implemented in incentive component and smart contract. For this to be possible the tokens can not be directly transferred to the actors wallet, but rather allocated to them for later verification.

Fiat conversion

The conversion of tokens to fiat is out of scope for the DVCT building block. This functionality should be defined by the dataspace, possibly through integration with payment gateways or exchange services.

Example Incentives

To better understand how points can be distributed in the ecosystem, let’s take a look at example incentives that could be used.

These are only a sample of all the incentives that could be used for token distribution with the DVCT

To simulate the incentive distribution, let’s consider three different scenario and apply different incentive distribution rules to scenarios related to training and skills management, where participants include data providers, AI/service providers, and use case orchestrators. In these scenarios, the focus is on how data and AI services are utilized to enhance training programs and manage skills development, with 100 points to be distributed among the participants (points are provided by data consumer).

Scenario 1: One Data Provider and One AI/Service Provider Scenario Overview:

Incentive Distribution:

Scenario 2: One Data Provider, Several AI/Service Providers, and Use Case Orchestrator Scenario Overview:

Incentive Distribution:

Scenario 3: Multiple Data Providers, One Service Provider

Scenario Overview:

Incentive Distribution:

Data Provider 80 Points:

Summary of Distribution in Points: Scenario 1 (One Data Provider, One AI/Service Provider):

Scenario 2 (One Data Provider, Several AI/Service Providers, Use Case Orchestrator):

Scenario 3 (Multiple Data Providers, Service provider, Use Case Orchestrator):

These examples show how the 100 points can be distributed in training and skills management scenarios, ensuring that each participant is fairly rewarded based on their role and contribution.

Integrations

In order to make the BB function, the integration with other BB is expected:

Direct Integrations with Other BBs

Integrations via Connector

Relevant Standards

Data Format Standards

Mapping to Data Space Reference Architecture Models

DSSC - based on DSSC, this Building block is part:

IDS Data Sharing and data exchange: see 2.4 Data Exchange and Data Sharing.

Input

Input data:

{
  "dvctId": "connector_id",
  "usecaseContractId": "use_case_contract_id",
  "usecaseContractTitle": "use_case_contract_title",
  "extraIncentiveForAIProvider": {
    "numPoints": 10,
    "factor": 1,
    "factorCheck": false
  },
  "contractId": "contract_id",
  "dataId": "data_id",
  "dataProviderId": "data_provider_id",
  "dataConsumerId": "data_consumer_id",
  "dataConsumerIsAIProvider": false,
  "prevDataId": "data_id",
  "incentiveForDataProvider": {
    "numPoints": 5,
    "factor": 1,
    "factorCheck": false
  }
}

Data Value Chain Tracker visualization

Data Value Chain Tracker digital incentives distribution

The distribution of digital incentives distribution should be based on the contract, defined by the use case orchestrator or between data provider and data consumer. Example of incentives json input defined within the contract:

incentive for AI provider

{
  "extraIncentiveForAIProvider": {
    "numPoints": 10,
    "factor": 1.0,
    "factorCheck": false
  }
}

incentive from data consumer to data provider

{
  "incentiveForDataProvider": {
    "numPoints": 5,
    "factor": 1.0,
    "factorCheck": false
  }
}

To further develop the integration of other aspects (e.g. data quality) for the incentive mechanism, the factor percentage (0.0 to 1.0) and the factorCheck object are added. By default, the incentive points are awarded in full (factor = 1,0) and without further checking of the incentive effect.

Architecture

Component Descriptions:

  1. DataProvider: Entities that supply data to the system.
  2. DataConsumer: Entities that use data provided by DataProviders.
  3. DVCT_Core: Central logic component that tracks data usage, creates data nodes and chains between data usage nodes.
  4. Blockchain: Ensures data immutability and transaction verification.
  5. Database: Stores non-blockchain data records and manages queries.
  6. UserInterface: Provides visualizations of data lineage, data usages information, points/token information and manages user interactions.
  7. ContractManagement: Manages digital contracts that define incentive models.
  8. API_Gateway: Manages all incoming and outgoing API requests and handles user authentication.
  9. Incentive Engine: Handles the calculation and distribution of tokens or points as per contractual agreements.
  10. Logging and Monitoring: Records system activities and monitors performance to ensure optimal operation and aid in troubleshooting.
  11. Error Handling and Recovery: Implements strategies to manage system errors and restore normal operations after failures, ensuring system resilience and reliability.
---
title: Architecture components
---
classDiagram
    class DataProvider {
        +Provide data()
        +Update data()
    }
    class DataConsumer {
        +Request data()
        +Receive data()
    }
    class DVCT_Core {
        +Track data usage()
        +Create data nodes()
        +Create chains()
    }
    class Blockchain {
        +Store immutable records()
        +Query records()
    }
    class Database {
        +Store data records()
        +Query data()
    }
    class UserInterface {
        +Display data lineage()
        +Manage user accounts()
    }
    class ContractManagement {
        +Manage contracts()
        +Define incentive models()
    }
    class IncentiveEngine {
        +Calculate incentives()
        +Distribute tokens or points
    }
    class API_Gateway {
        +Route requests()
        +Authenticate users()
    }
    class LoggingMonitoring {
        +Log operations()
        +Monitor system performance()
    }
    class ErrorHandlingRecovery {
        +Handle system errors()
        +Recover from failures()
    }

    DataProvider --|> DVCT_Core : provides data to
    DataConsumer --|> DVCT_Core : consumes data from
    DVCT_Core --|> Blockchain : uses
    DVCT_Core --|> Database : uses
    DVCT_Core --|> UserInterface : outputs to
    DVCT_Core --|> ContractManagement : interacts with
    DVCT_Core --|> IncentiveEngine : uses
    API_Gateway --|> DVCT_Core : interfaces with
    UserInterface --|> API_Gateway : connects through
    ContractManagement --|> Blockchain : records contracts on
    IncentiveEngine --|> ContractManagement : gets rules from
    IncentiveEngine --|> Blockchain : records transactions on
    DVCT_Core --|> LoggingMonitoring : uses for logging
    DVCT_Core --|> ErrorHandlingRecovery : uses for managing errors

Dynamic Behaviour

The sequence diagrams below describe possible DVCT to the basic B2B Connector flows.

---
title: Data Exchange for Data Value Chain Tracker
---

sequenceDiagram
    participant o as Orchestrator
    participant dw1 as Orchestrator wallet

    participant dp1 as Data provider 1
    participant dw-p1 as Wallet Provider 1
    participant pc1 as Connector provider 1
    participant dvct-p1 as DVCT provider 1

    participant dp2 as Data provider 2
    participant dw-p2 as Wallet Provider 2
    participant pc2 as Connector provider 2
    participant dvct-p2 as DVCT provider 2

    participant cot as Contract Service
    participant wal as Billing/Wallet Service
    participant cat as Catalogue Service

    participant dvct-c1 as DVCT consumer/AI provider 1
    participant cc1 as Connector Consumer 1
    participant dw-c1 as Wallet Consumer 1
    participant c1 as Consumer/AI provider 1

    participant dvct-c2 as DVCT consumer/AI provider 2
    participant cc2 as Connector Consumer 2
    participant dw-c2 as Wallet consumer 2
    participant c2 as Consumer/AI provider 2

    Note over dw1: points/token available
    Note over dw-c1: points/token available
    Note over dw-c2: points/token available
    activate cat
    o -) cat: Trigger data exchange, trigger data exchange, define the use case, data flow & points distribution
    Note over cat: use case
    dp1 -) cat: join the use case
    cat -) pc1: contract and data exchange information
    dp2 -) cat: join the use case
    cat -) pc2: contract and data exchange information
    c1 -) cat: join the use case
    cat -) cc1: contract and data exchange information
    c2 -) cat: join the use case
    cat -) cc2: contract and data exchange information
    deactivate cat
    cc1 -) pc1: data request (with contract)
    pc1 -) cot: contract verification and policies
    cot -) pc1: verified contract & policies
    Note over pc1: policy verification & access control
    pc1 -) dp1: get data
    Note over dp1: Raw data DP1
    dp1 -) pc1: data
    pc1 -) cc1: data DP1
    Note over cc1: policy verification & access control
    cc1 -) c1: consume data DP1

    cc1 -) dvct-c1: data consume trigger DVCT
    dvct-c1 -) cc1: get DP1 information
    cc1 -) dvct-c1: DP1 metadata info
    dvct-c1 -) dvct-c1: [Node1] create Node based on DP1 metadata
    Note over dvct-c1: A node consist of metadata, prevRoot, and children
    dvct-c1 -) cc1: get data-type output [chain-data]
    cc1 -) dvct-c1: data-type output [chain data]
    dvct-c1 -) dvct-c1: [Node2] create Node based on data-type output defined in contract
    dvct-c1 -) dvct-c1: create chain between Node1 and Node2
    Note over dvct-c1: the chain = [Node1 is prevRoot of Node2]
    Note left of dvct-c1: visualization of chain between Node1 and Node2
    dvct-c1 -) cc1: get data for point distribution
    cc1 -) dvct-c1: data of point distribution

    dvct-c1 -) dw1: get point(s) as AI provider based on contract
    dw1 -) dw1: reduce point(s)
    dw1 -) dvct-c1: point(s)
    dvct-c1 -) dw-c1: distribute point
    dvct-c1 -) dw-c1: get point(s) for data usage
    dw-c1 -) dw-c1: reduce point(s)
    dw-c1 -) dvct-c1: point(s)
    dvct-c1 -) dw-p1: distribute point

    dvct-c1 -) cc1: request to update prevRoot(if any) based on data-output [Node2]
    cc1 -) dp1: send request and prevRoot node(s) data [Node1]
    dp1 -) dvct-p1: chain-data update request, check if Node1 is already exist
    alt is exist and has no prevRoot
        dvct-p1 -) dvct-p1: update Node
    else is exist and has prevRoot
        loop prevNodes
            dvct-p1 -) dvct-p1: update Node and send update request to all prevRoot
        end
    else is not exist
        dvct-p1 -) dvct-p1: create a new Node for Node1
    end

    cc2 -) pc1: data request (with contract)
    pc1 -) cot: contract verification and policies
    cot -) pc1: verified contract & policies
    Note over pc1: policy verification & access control
    pc1 -) dp1: get data
    Note over dp1: Raw data DP1
    dp1 -) pc1: data
    pc1 -) cc2: data DP1

    cc2 -) pc2: data request (with contract)
    pc2 -) cot: contract verification and policies
    cot -) pc2: verified contract & policies
    Note over pc2: policy verification & access control
    pc2 -) dp2: get data
    Note over dp2: chain data DP2 [Node2]
    dp2 -) pc2: data
    pc2 -) cc2: data DP2 [Node2]

    Note over cc2: policy verification & access control
    cc2 -) c2: consume data DP1 and DP2
    Note over c2: data-type output is visualized-data/final data

    cc2 -) dvct-c2: data consume trigger DVCT
    dvct-c2 -) cc2: get DP1 & DP2 information
    cc2 -) dvct-c2: DP1 & DP2 metadata info
    dvct-c2 -) dvct-c2: [Node1 and Node2] create Nodes based on DP1 & DP2 metadata
    Note over dvct-c2: A node consist of metadata, prevRoot, and children
    dvct-c2 -) cc2: get data-type output [visualized-data]
    cc2 -) dvct-c2: data-type output [visualized-data]
    dvct-c2 -) dvct-c2: [Node3] create Node based on data-type output defined in contract
    dvct-c2 -) dvct-c2: create chain between Node3 and prevNode [Node1 & Node2]
    Note over dvct-c2: the chain = [Node1 and Node2 are prevRoot of Node3]
    Note left of dvct-c2: visualization of chain between Node3 and prevRoot
    dvct-c2 -) cc2: get data for point distribution
    cc2 -) dvct-c2: data of point distribution

    dvct-c2 -) dw1: get point(s) as AI provider based on contract
    dw1 -) dw1: reduce point(s)
    dw1 -) dvct-c2: point(s)
    dvct-c2 -) dw-c2: distribute point
    dvct-c2 -) dw-c2: get point(s) for data usage
    dw-c2 -) dw-c2: reduce point(s)
    dw-c2 -) dvct-c2: point(s)
    dvct-c2 -) dw-p2: distribute point

    dvct-c2 -) cc2: request to update prevRoot(if any) based on data-output [Node3]
    cc2 -) dp2: send request and prevRoot node(s) data [Node1 and Node2]
    dp2 -) dvct-p2: chain-data update request, check if prevRoot node is already exist
    alt is exist and has no prevRoot
        dvct-p2 -) dvct-p2: update Node
    else is exist and has prevRoot
        loop prevNodes
            dvct-p2 -) dvct-p2: update Node and send update request to all prevRoot
        end
    else is not exist
        dvct-p2 -) dvct-p2: create a new Node for Node1
    end

To make the diagram smaller, more manageable parts, ensuring it remains comprehensible and easy to follow on smaller screens, we divided the process into different main processes:

sequenceDiagram
    participant o as Orchestrator
    participant cat as Catalogue Service
    participant dp1 as Data provider 1
    participant dp2 as Data provider 2
    participant pc1 as Connector provider 1
    participant pc2 as Connector provider 2
    participant cc1 as Connector Consumer 1
    participant cc2 as Connector Consumer 2
    participant c1 as Consumer/AI provider 1
    participant c2 as Consumer/AI provider 2

    activate cat
    o ->>+ cat: Define use case, data flow & points distribution
    dp1 ->>+ cat: Join use case
    dp2 ->>+ cat: Join use case
    c1 ->>+ cat: Join use case
    c2 ->>+ cat: Join use case
    cat -->>- pc1: Provide contract and data exchange information
    cat -->>- pc2: Provide contract and data exchange information
    cat -->>- cc1: Provide contract and data exchange information
    cat -->>- cc2: Provide contract and data exchange information
    deactivate cat


sequenceDiagram
    participant pc1 as Connector provider 1
    participant dp1 as Data provider 1
    participant cc1 as Connector Consumer 1
    participant c1 as Consumer/AI provider 1
    participant dvct-c1 as DVCT consumer/AI provider 1
    participant cot as Contract Service

    cc1 ->> pc1: Data request (with contract)
    pc1 ->> cot: Verify contract
    cot -->> pc1: Verified contract & policies
    pc1 ->> dp1: Request data
    dp1 -->> pc1: Provide raw data DP1
    pc1 -->> cc1: Transfer data DP1
    cc1 ->> c1: Consume data DP1
    cc1 ->>+ dvct-c1: Trigger data consume DVCT
    cc1 ->> dvct-c1: Get DP1 metadata info
    dvct-c1 ->> dvct-c1: Create Node based on DP1 metadata


sequenceDiagram
    participant dvct-c1 as DVCT consumer/AI provider 1
    participant dw1 as Orchestrator wallet
    participant dw-c1 as Wallet Consumer 1
    participant dw-p1 as Wallet Provider 1
    participant dp1 as Data provider 1
    participant dvct-p1 as DVCT provider 1
    participant cc1 as Connector Consumer 1

    dvct-c1 ->> dw1: Request point(s) based on contract
    dw1 -->> dvct-c1: Provide point(s)
    dvct-c1 ->> dw-c1: Distribute point(s)
    dvct-c1 ->> dw-p1: Distribute point(s)
    dvct-c1 ->> cc1: Request update to prevRoot based on data output
    cc1 ->> dp1: Send request and prevRoot node(s) data
    dp1 ->> dvct-p1: Update chain data, check existence
    dvct-p1 ->> dvct-p1: Handle Node creation or update

Configuration & Deployment Settings

The configuration and deployment setting for Data Value Chain Tracker (DVCT), consist of:

  1. Repository Setup: the source code will be hosted on GitHub or GitLab, allowing for version control and collaborative development.
  2. Configuration Management: Environment variables for sensitive or environment-specific settings (e.g., database credentials) should be centralized in specific files, employ configuration files (like config.json, .env, or YAML files) to manage application settings, which can be easily adjusted without changing the code.
  3. Dependency Management: Utilize a package manager such as npm for JavaScript or pip for Python to manage libraries and their versions. a requirements.txt or package.json file should be included to automate the installation of dependencies.
  4. Database Configuration: Set up a relational or NoSQL database with scripts for initialization and migration. Tools like Docker can be used to containerize database environments, enhancing portability and consistency across development, testing, and production environments.
  5. Deployment:Use continuous integration/continuous deployment (CI/CD) pipelines via GitHub Actions, to automate testing and deployment. Use container orchestration tools like Kubernetes or Docker Swarm.
  6. Monitoring and Maintenance: Implement logging and monitoring using tools like Prometheus, Grafana, or ELK Stack to keep track of the system’s health and performance. Regular updates and security patches to dependencies should be managed through the chosen package managers and monitored via the CI/CD pipeline.

Third Party Components & Licenses

OpenAPI Specification

The current specification can be found here.

Test Specification

Test Plan

The test plan for the Data Value Chain Tracker (DVCT) aims to ensure the system’s integrity and performance through a comprehensive approach. It includes correctness tests for accurate data representation, reliability tests for system stability and data integrity, tests data immutability and scalability, back and forward tracking tests to verify accurate data lineage, and incentives distribution tests to ensure compliance and fairness based on contractual agreements.

To check the result of the value chain creation, the DVCT should create a node for the data usage in output data json format after the data is used on the data consumer side (PDC consumer will trigger DVCT). Each time information about the prevDataId is present in the input data, the DVCT checks whether the prevDataId already exists (as a nodeId) within the value chain node. If this is the case, the childNode of the prevDataId is updated with the new dataId as a child node.

Output data:

{
  "nodeId": "node_id",
  "dataId": "data_id",
  "nodeMetadata": {
    "dvctId": "connector_id",
    "usecaseContractId": "use_case_contract_id",
    "dataProviderId": "data_provider_id",
    "dataConsumerId": "data_consumer_id",
    "incentiveReceivedFrom": [
      {
        "organizationId": "organization_id",
        "numPoints": 5,
        "contractId": "contract_id"
      },
      {
        "organizationId": "organization_id",
        "numPoints": 5,
        "contractId": "contract_id"
      }
    ]
  },
  "prevNode": [
    { "nodeId": "node_id", "@nodeUrl": "https://url-to-nodeId/nodeId" },
    { "nodeId": "node_id", "@nodeUrl": "https://url-to-nodeId/nodeId" }
  ],
  "childNode": [
    { "nodeId": "node_id", "@nodeUrl": "https://url-to-nodeId/nodeId" },
    { "nodeId": "node_id", "@nodeUrl": "https://url-to-nodeId/nodeId" }
  ]
}

Back and forward chain tracking

Back and forward chain tracking in the context of the Data Value Chain Tracker (DVCT) refers to the system’s ability to trace data usage throughout its lifecycle. Forward tracking enables monitoring of how data is used, transformed, or combined from its initial state to subsequent states, including indirect usages in various use cases. It helps determine where, when, and in which use case the data was utilized.

Backward tracking, on the other hand, allows tracing back to the data’s origin up to three levels, identifying the primary source and any intermediate stages it has passed through. This feature ensures transparency and accountability in data handling, allowing stakeholders to see both the downstream implications of data they provide and the upstream origins of data they use. This capability is critical for auditability, compliance, and verifying the integrity of data transformations and linkages in complex systems.

Component-level testing

These tests will check the interactions between DVCT and external systems like the Data Space Connector and Contract Service to ensure data flows correctly through the system and meets all business requirements.

Incentives Distribution Tests:

Test the logic and execution of digital incentives distribution to ensure it complies with the contractual agreement. Simulate various contractual scenarios to ensure incentives are calculated and distributed accurately and transparently.

Partners & roles

imc AG (website): As Building Block Lead, responsible for leading the design of the DVCT Building Block, drafting the initial design specifications for value tracking and ensuring that the development is in line with Prometheus-X and other Dataspaces standards such as IDSA and GAIA-X. imc AG is responsible for these components:

Visiontrust (website): Responsible in the implementation phase, preparing the development environment for the DVCT, ensuring smooth communication and interaction of the DVCT with the corresponding building blocks and the PTX dataspace connector. Identification of data processing input and output. Visiontrust will take the lead in developing these components for the DVCT:

Nomadlabs (website): Responsible in the implementation phase for incentivizing data usage, integration of smart contracts, value-chain and blockchain technology within the DVCT. Overall, Nomadlabs will manage the development of these components for the DVCT:

Each partner is responsible for error handling and correction within the developed components.

Usage in the dataspace

DVCT is useful for tracking data usage and can thus provide greater benefit to members of the data space. DVCT will support the skills service chain and not only help data providers recognize the value of their data to business and society, but also distribute incentives across the network.

Reference