Confidence-enabled data storage systems with annotation bookmarks
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Solution Overview
Problem
Data storage systems lack awareness of confidence information associated with stored data, leading to unawareness of data quality and potential access to low-confidence data, and they are unable to report overall data confidence scores.
Innovation Solution
Implementing an annotation bookmark system within a data confidence fabric to notify a confidence assessor for updating confidence-aware storage systems, allowing for the assignment and reporting of confidence scores, and enabling awareness of data confidence levels across the storage system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a Data Confidence Fabric annotates trust operations as data flows through the fabric, then confidence scores are derived from the annotations, but the storage system remains unaware of any confidence information associated with the data
Solution Approach 1:
The patent introduces a confidence assessor as an intermediary component that bridges the Data Confidence Fabric and the storage system. The assessor receives annotated data with confidence information from the fabric, processes this information, and updates the storage system's metadata accordingly. This intermediary resolves the disconnect by translating the fabric's confidence annotations into storage-system-understandable formats, ensuring confidence information is preserved throughout the data lifecycle.
Solution Approach 2:
The system implements a feedback mechanism where the confidence assessor continuously monitors data flowing through the storage system, evaluates confidence levels based on annotations from the Data Confidence Fabric, and feeds this confidence information back into the storage metadata. This closed-loop feedback ensures that the storage system maintains awareness of confidence information associated with stored data, enabling informed data management decisions.
2Ease of operation
If the storage system stores data without confidence awareness, then storage operations are simple, but applications cannot make informed decisions about data quality
Solution Approach 1:
The system performs preliminary actions by automatically assigning confidence scores to data during the storage process itself, rather than requiring separate manual assessments later. The confidence assessor evaluates data and attaches confidence metadata at the point of ingestion, so that confidence information is already available when data is stored. This preliminary action maintains operational simplicity while ensuring data quality awareness is built-in from the start.
Solution Approach 2:
The storage system enhances itself by automatically tracking and managing confidence information without requiring external intervention. The confidence assessor operates autonomously, monitoring data flows, evaluating confidence levels, and updating metadata independently. This self-service capability allows the system to maintain both operational simplicity and data quality assurance, as the confidence management happens automatically within the existing storage infrastructure.
3Measurement precision
If confidence information is tracked through the Data Confidence Fabric, then data trustworthiness is measured, but the storage system and applications remain unaware of confidence levels
Solution Approach 1:
The confidence assessor serves as a mediator that connects the Data Confidence Fabric's measurement capabilities with the storage system and applications. It receives precise confidence measurements from the fabric, processes this information, and makes it available to the storage system through metadata updates. This intermediary ensures that the detailed confidence information measured by the fabric is not lost but is instead transmitted and made accessible to downstream systems that need it for decision-making.
Data Source
AI summary
One example method includes receiving data from a node of a data confidence fabric, in response to receipt of the data, creating an annotation bookmark that (i) identifies a storage node at which the data was received and that (ii) uniquely identifies the data, annotating a copy of the data with the annotation bookmark, and storing, at the storage node, the copy of the data in association with the annotation bookmark.