Data Confidence Fabric Dynamic Scoring

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Solution Overview

Problem

Existing systems face challenges in accurately scoring or ranking data trustworthiness due to limitations in calculation methods, resource usage, and lack of contextual information provided to applications, which can lead to unidentified trust failures and inadequate data utilization.

Innovation Solution

A data confidence fabric (DCF) system that scores or ranks data by employing trust insertion technologies, using a combination of static and dynamic layers, weighting tables, and programmable scoring algorithms to generate cumulative confidence scores, providing transparent and context-aware trustworthiness assessments.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If conventional scoring methods are used to determine data trustworthiness, then the scoring process is simple, but the scores do not provide sufficient context to applications and fail to identify trust failures

Engineering Contradiction:

Improvecontext informationVSAvoidscoring system complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent segments the trustworthiness scoring process into multiple independent trust insertion technologies (e.g., cryptographic verification, data provenance tracking, anomaly detection). Each technology evaluates specific aspects of data trustworthiness and generates separate scores, which are then aggregated. This segmentation allows applications to understand which specific trust criteria were satisfied or failed, providing detailed context without requiring a monolithic complex system.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent adds a new dimension to traditional scoring by introducing multi-dimensional trust metrics. Instead of a single scalar score, the system evaluates data across multiple dimensions (authenticity, integrity, freshness, source reliability) and provides scores for each dimension. This dimensional expansion enriches the information provided to applications while maintaining manageable complexity through modular evaluation.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

2Measurement precision

If computing resources are used to compute trust values, then accurate trust scores are generated, but the use of computing resources is negatively impacted

Engineering Contradiction:

Improvetrust score accuracyVSAvoidcomputing resource consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The patent implements preliminary trust insertion at the data source or intermediate processing stages, before data reaches the application. Trust metrics are computed and attached to data during ingestion or transformation phases, distributing the computational load across the data pipeline rather than concentrating it at the application layer. This preliminary action reduces the computing burden on applications while maintaining accurate trust scores.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system enables data sources and intermediate processing components to self-evaluate their own data for trustworthiness using lightweight trust insertion technologies. Each component autonomously computes relevant trust metrics for its output data, reducing the need for centralized verification and minimizing overall computing resource consumption while preserving measurement precision.

Inventive Principle:

Principle #25Self-service

3Reliability

If multiple trust insertion technologies are applied to data, then comprehensive trust assessment is achieved, but the complexity of determining which technologies successfully inserted trust increases

Engineering Contradiction:

Improvetrust assessment comprehensivenessVSAvoidtrust insertion verification difficulty

Core Design Contradiction:

ReliabilityVSDifficulty of detecting and measuring

Solution Approach 1:

The patent implements feedback mechanisms where each trust insertion technology reports its successful application status and generated scores back to a central aggregation point. The system tracks which technologies were applied to each data item and their individual contributions to the overall trust assessment. This structured feedback loop makes it straightforward to verify successful trust insertions and aggregate results, even when multiple diverse technologies are employed.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent employs a universal trust insertion framework that can accommodate multiple different trust technologies through a common interface and aggregation mechanism. The system uses standardized data structures and protocols that work across diverse trust insertion technologies, simplifying the tracking and verification process. This multi-functional approach allows comprehensive trust assessment while maintaining uniformity in how success is detected and measured across different technologies.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS11308076B2Dynamic scoring in data confidence fabrics

Publication Date: 2022.04.19 EMC IP HLDG CO LLC

AI summary

A data confidence fabric (DCF) is disclosed. The DCF may include a static configuration layer, a dynamic trust insertion layer, and a programmable scoring layer. The DCF may also include edge devices and applications that use the ingested data in some instances. The operation of the DCF allows data to be ingested and associated with a confidence or trustworthiness score. The confidence score can be used by applications that desire access to and use of the ingested data.