Hybrid Platform for Data Artifact Trust Scoring
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Solution Overview
Problem
There is a need for an efficient and expedient way to perform testing of data artifacts to validate their trustworthiness within a network environment.
Innovation Solution
A hybrid centralized-decentralized computing platform is used for electronic data artifact testing, which involves uploading data artifacts to an artifact testing platform. The platform employs various validators, including AI-based modules, to analyze the data artifacts and generate confidence levels. These confidence levels are aggregated to produce a final trust score, providing a secure validation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional centralized computing platforms are used for data artifact testing, then system control and coordination are simplified, but processing speed and validation efficiency are insufficient
Solution Approach 1:
The system segments validation tasks into multiple independent validator modules that can process data artifacts in parallel. Each validator specializes in specific aspects of data artifact analysis, allowing simultaneous processing without requiring complex centralized coordination for every operation.
Solution Approach 2:
The system transitions from a single-dimensional centralized processing model to a multi-dimensional hybrid architecture combining centralized coordination with decentralized execution. This allows validation operations to occur across multiple levels simultaneously, improving throughput while maintaining control.
2Measurement precision
If multiple validators are used to analyze data artifacts, then validation accuracy and trust score reliability are improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis to identify key characteristics of data artifacts before invoking validators. Based on these characteristics, it selectively activates only the most relevant validators, avoiding unnecessary processing time while maintaining accurate trust score generation.
Solution Approach 2:
The system uses a dynamic approach where the number and type of validators invoked can be adjusted based on the specific data artifact being analyzed. For high-risk artifacts, more validators are activated for thorough analysis, while low-risk artifacts receive streamlined validation, optimizing the balance between accuracy and processing time.
Data Source
AI summary
A system is provided for electronic data artifact testing using a hybrid centralized-decentralized computing platform. In particular, the system may comprise an artifact testing platform that may be accessed by users and computing devices within a network. Users may upload a data artifact to the artifact testing platform to be validated by the system. The system may then use a number of different validators (e.g., artificial intelligence-based modules) that may read the data artifact and/or the associated metadata and generate a confidence level based on the characteristics of the data artifact. The confidence levels from each validator may be aggregated to generate a final trust score for the data artifact. If the final trust score is below a first threshold, blocking the user computing device from opening the electronic file and if the final trust score is below a second threshold, performing network segmentation of the user computing device.