Entity Data Structure Connectivity Score Modification
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Solution Overview
Problem
Existing data structures for representing entity relationships are limited in capturing indirect connections among entities, leading to inaccurate risk assessments as they primarily focus on explicitly identified relationships, neglecting a wider network of interconnections.
Innovation Solution
Modifying data structures to include derived relationships among entity data objects by computing connectivity scores based on explicit and indirect links, allowing for the representation of interconnectivity among groups of entities and generating recommendations for modifying relationships based on these scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data structures use only explicitly identified relationships to represent entity connections, then data structure simplicity is maintained, but risk assessment accuracy deteriorates due to failure to capture indirect relationships
Solution Approach 1:
The patent segments entity connections into two distinct types: explicit relationships (directly recorded in the database) and implicit relationships (derived through connectivity analysis). This segmentation allows the system to maintain the simplicity of explicit relationship storage while adding complexity only where necessary for accurate risk assessment, resolving the contradiction between data structure simplicity and risk assessment accuracy.
Solution Approach 2:
The patent introduces connectivity scores as an intermediary mechanism that bridges explicit and implicit relationships. These scores quantify the strength of indirect connections between entities through intermediate entities, enabling accurate risk assessment without requiring direct explicit relationships. The intermediary connectivity score system captures indirect relationships while maintaining the underlying simple data structure of explicit connections.
2Loss of information
If data structures include only direct entity relationships, then data storage efficiency is improved, but information completeness deteriorates due to missing indirect connections
Solution Approach 1:
The patent performs preliminary connectivity analysis to identify and quantify implicit relationships before they are needed for risk assessment. By pre-computing connectivity scores based on existing explicit relationships, the system captures indirect connections without storing redundant relationship data. This preliminary action ensures information completeness while avoiding excessive data storage requirements.
Solution Approach 2:
The patent transforms the representation of relationships by introducing connectivity scores as a new parameter that derives implicit relationship strength from explicit relationship patterns. Instead of storing additional relationship records, the system changes the parameter space by adding computed connectivity metrics, thereby capturing more relationship information without proportionally increasing data storage requirements.
3Measurement precision
If connectivity analysis is performed on all entity pairs, then relationship detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies partial action by computing connectivity scores selectively rather than for all possible entity pairs. The system focuses computational resources on entities and relationships that are relevant to specific risk assessment queries, computing connectivity scores only when needed based on the analysis requirements. This approach maintains high relationship detection accuracy while reducing unnecessary computational complexity.
Data Source
AI summary
In some aspects, an interconnectivity evaluation system identifies, in a data structure with data indicating links among entity data objects, first links between a first entity data object and additional entity data objects and second links between a second entity data object and the additional entity data objects. A connectivity score for the first and second entity data objects, which indicates a weight of a relationship between the first and second entity data objects, is computed based on the first links and the second links. Data in the data structure is modified to indicate a relationship between the first and second entity data objects. The relationship is associated with the computed connectivity score. If a difference between the connectivity score and a desired connectivity score exceeds a threshold, a recommendation is outputted to modify the connectivity score by changing at least some of the links among the entity data objects.