Provenance Data Model for Smart Contract Analytics Transparency
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Solution Overview
Problem
Current smart contract execution systems lack provenance and transparency, making it difficult to verify whether analytics are executed correctly and what data they use to generate outcomes, leading to potential inconsistencies and loss of confidence between parties in value-based agreements.
Innovation Solution
Implementing a mechanism that uses a provenance data model to verify analytic outcomes by linking entries across data models, ensuring that the correct analytics are executed with the right data, and storing this information on a blockchain ledger for immutability and transparency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If smart contract execution systems execute analytics automatically, then productivity is improved, but reliability deteriorates due to lack of provenance and transparency
Solution Approach 1:
The patent introduces a provenance data model as an intermediary layer between analytics execution and blockchain storage. This mediator captures detailed information about data sources, transformations, and execution contexts, enabling verification of analytics outcomes without manual intervention. The provenance model acts as a bridge that maintains both automation and trustworthiness.
2Productivity
If analytics execution is automated without verification mechanisms, then productivity increases, but measurement precision deteriorates due to inability to verify outcomes
Solution Approach 1:
The system performs preliminary actions by capturing provenance information (data sources, transformations, execution contexts) before analytics outcomes are finalized. This advance documentation enables subsequent verification of whether analytics were executed correctly with the right data, maintaining measurement precision while preserving automation.
3Device complexity
If no provenance tracking is implemented, then device complexity is reduced, but loss of information increases regarding data usage and transformation
Solution Approach 1:
The patent segments the system into distinct components: analytics execution engine, provenance data model, and blockchain ledger. The provenance model is divided into specific fields (data sources, transformations, execution contexts). This segmentation allows tracking of provenance information without overwhelming system complexity, as each component has a specific responsibility.
4Reliability
If manual verification of analytics outcomes is required, then reliability improves, but productivity deteriorates due to increased time and human intervention
Solution Approach 1:
The system implements self-service verification by automatically capturing, storing, and making accessible provenance information that enables parties to verify analytics outcomes independently. The provenance data model automatically documents execution details, allowing systems to self-verify without manual intervention, thus maintaining both reliability and productivity.
Data Source
AI summary
A mechanism is provided to review and verify provenance of analytic execution by a contract analytic binding and provenance system. The mechanism is activated to execute a set of analytics for a contract and verifies outcomes of the analytics before writing them to a blockchain network. The mechanism evaluates the provenance data records stored on peer ledgers and establishes transparency of the outcomes by validating consensus between characteristics of the provenance data.