Waterfall Confidence Visualization Gap Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional data confidence fabrics (DCFs) lack the ability to identify and resolve gaps where trust insertion components are absent or non-functional, leading to sub-optimal confidence scores and a lack of visibility into confidence coverage throughout the data's journey.
Innovation Solution
The implementation of a 'waterfall visualization' system that identifies nodes in the DCF where trust insertion information is needed, detects gaps in trust insertion, and automatically deploys trust insertion components to address these gaps, thereby enhancing data confidence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If trust insertion components are added to improve confidence coverage, then confidence score improves, but device complexity increases
Solution Approach 1:
The system segments the DCF into discrete hops or stages, allowing trust insertion components to be selectively added at specific segments rather than throughout the entire system. This enables targeted confidence improvement without uniformly increasing complexity across all components.
Solution Approach 2:
Different segments of the DCF are assigned different trust insertion requirements based on local needs. The system identifies specific hops where trust insertion is most critical and applies confidence measures only at those locations, optimizing the balance between confidence improvement and complexity management.
2Reliability
If comprehensive trust insertion components are deployed throughout the DCF, then confidence coverage improves, but the overhead and redundancies increase
Solution Approach 1:
Instead of deploying trust insertion components at every possible hop, the system applies partial action by only adding components where confidence gaps are identified. This selective approach provides sufficient confidence coverage without the excessive overhead of universal deployment.
Solution Approach 2:
The system performs preliminary analysis of the DCF to identify gaps in confidence coverage before deploying trust insertion components. This preliminary action allows the system to target resources efficiently, avoiding unnecessary overhead by only implementing trust measures where they are most needed.
3Reliability
If applications have detailed requirements for data handling (encryption, TEE processing), then data trustworthiness improves, but the difficulty of detecting and measuring confidence gaps increases
Solution Approach 1:
The system implements feedback mechanisms that continuously monitor DCF hops and compare actual trust insertion against required trust insertion. This feedback loop automatically detects confidence gaps by measuring the difference between what should be present and what actually is present, making complex trust requirements measurable and detectable.
Solution Approach 2:
The system replaces complex manual verification of trust requirements with automated confidence vectors and gap detection algorithms. This substitution transforms the difficult task of measuring complex trust compliance into a more manageable computational process that automatically identifies gaps.
Data Source
AI summary
One example method includes generating respective confidence vectors for respective hops associated with a data confidence fabric (DCF), and each hop is associated with one or more trust insertion technologies, identifying a deviation from one of the confidence vectors, generating a waterfall visualization that identifies a location in the DCF, and a type, of the deviation, automatically identifying a remediation action concerning the deviation, and automatically implementing the remediation action in a node of the DCF.