Unified Resource Records for Detecting and Remediating Data Gaps
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Solution Overview
Problem
Resource management systems face challenges in maintaining accurate and complete asset data across discontiguous networks due to fragmented data sources, leading to inefficiencies, poor decision-making, increased operational costs, and cybersecurity risks.
Innovation Solution
A comprehensive digital platform that detects and remediates data gaps by composing unified records, using advanced ensemble models to prioritize remediation efforts, and automatically generating corrective actions based on resource impact scores and failure risk, integrating with existing workflows to ensure timely data integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is collected from multiple fragmented systems with varying data models, then data coverage is improved, but data consistency and accuracy deteriorate
Solution Approach 1:
The system segments data validation and gap detection into specialized ensemble models that operate independently on different aspects of data quality (completeness, consistency, accuracy, timeliness). Each model focuses on specific data attributes, allowing parallel processing of multiple data sources while maintaining specialized validation logic for each data type and source characteristic.
Solution Approach 2:
The patent introduces an intermediary resource compliance process that acts as a mediator between fragmented data sources and the resource management system. This intermediary layer composes unified resource records by reconciling data from multiple sources, applying validation rules, and generating gap remediation requests, thereby ensuring data consistency without losing the benefits of multi-source data collection.
2Measurement precision
If manual data validation and remediation processes are used, then data accuracy can be improved, but time consumption and operational costs increase
Solution Approach 1:
The system implements self-service automation where the resource compliance process automatically detects data gaps, prioritizes them using ensemble models, generates remediation requests, and validates corrections without manual intervention. The system serves itself by continuously monitoring data quality, identifying issues, and triggering appropriate remediation workflows, eliminating the need for manual data validation while maintaining high accuracy standards.
Solution Approach 2:
The patent incorporates feedback loops where data quality metrics are continuously monitored, and remediation effectiveness is measured and fed back into the system. The ensemble models learn from validation results and adjust their prioritization criteria, creating a self-improving system that maintains high data accuracy while reducing time consumption through automated iterative refinement.
3Reliability
If comprehensive data validation is performed across all resource attributes, then data quality is improved, but system complexity increases
Solution Approach 1:
The system applies local quality by implementing attribute-specific validation rules and ensemble models tailored to different resource types and data sources. Rather than applying a uniform complex validation framework to all data, the system customizes validation depth and criteria based on the specific characteristics of each resource attribute, data source reliability, and business criticality, thereby maintaining high data quality without unnecessary system complexity.
Solution Approach 2:
The patent dynamically adjusts validation parameters and prioritization criteria based on resource criticality, data source trustworthiness, and gap severity. The ensemble models modify their validation thresholds and inspection depth according to changing conditions, allowing the system to maintain comprehensive validation where needed while reducing complexity for less critical data, thereby optimizing the balance between data quality and system complexity.
4Speed
If data gaps are addressed immediately upon detection, then data timeliness is improved, but resource prioritization efficiency deteriorates
Solution Approach 1:
The system performs preliminary prioritization of data gaps using ensemble models that assess resource criticality, failure risk, and business impact before remediation begins. By pre-ranking gaps based on their importance and urgency, the system prepares a prioritized remediation queue that guides subsequent actions, ensuring that the most critical data quality issues are addressed first while maintaining overall timeliness across all gaps.
Solution Approach 2:
The patent implements dynamic prioritization where the importance ranking of data gaps is continuously adjusted based on changing resource conditions, operational context, and remediation progress. The ensemble models recalculate priorities in real-time, allowing the system to adapt to new information and changing business needs, thereby maintaining both timeliness and remediation efficiency through flexible, condition-based resource allocation.
Data Source
AI summary
The disclosed technology includes systems and methods for addressing data gaps in fragmented data collections generated by discontiguous networks comprising multiple systems distributed across digital and physical locations. They include composing a record of a resource by matching identifiers across one or more data sources. A model is used to identify data gaps in the record by comparing first values of first attributes against a reference comprising second attributes and second values retrieved from the data sources. Each data gap includes a nonconforming attribute with an invalid value and a fault type. The system generates a request to resolve the data gap by correcting the invalid value of the nonconforming attribute according to the identified fault type.