Contextual Relationship Mapping with Reusable Base Data Layers
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Solution Overview
Problem
Existing systems struggle to efficiently manage and store data corresponding to complex relationships between entities, particularly in financial services, due to varying definitions and regulations, leading to redundant data storage and increased computational resources.
Innovation Solution
A centralized system that generates and manages base layer data objects representing entities and relationships, using conditional rules to identify and store contextual relationships, reducing redundancy and adapting to changing definitions and regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is collected and stored for each specific relationship definition, then relationship data completeness is improved, but data redundancy and storage costs increase
Solution Approach 1:
The patent segments relationship data into two layers: a shared base layer containing entity attributes and relationship facts, and context-specific layers containing only the contextual interpretation rules. This segmentation eliminates redundant storage of base data while maintaining completeness for each relationship context.
Solution Approach 2:
The base layer data objects are designed to serve multiple relationship contexts simultaneously. A single set of entity attributes and relationship facts can be reused across different relationship definitions by applying different contextual rules, making the data structure universal and multi-functional.
2Measurement precision
If multiple relationship definitions are maintained separately, then relationship-specific accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments relationship definitions into reusable base components and context-specific rule components. This allows precise relationship definitions through rule composition without duplicating entire relationship structures, reducing system complexity while maintaining accuracy.
Solution Approach 2:
The system enables dynamic composition of relationship definitions by combining base layer data with different contextual rules. Relationship definitions can be flexibly configured and modified by changing rules rather than restructuring entire relationship models, reducing complexity while preserving precision.
3Adaptability or versatility
If relationship data is collected comprehensively for all contexts, then adaptability to regulatory changes is improved, but computational resources increase
Solution Approach 1:
By segmenting data into shared base layer and context-specific rule layers, the system can adapt to regulatory changes by modifying only the relevant contextual rules without processing or storing redundant base data for all contexts, reducing computational resource usage.
Solution Approach 2:
The system performs preliminary organization of data into reusable base layer objects, so that when regulatory changes occur, only the necessary contextual rules need to be updated rather than reprocessing entire relationship datasets, improving adaptability while conserving computational resources.
Data Source
AI summary
Systems and method for relationship mapping may include server(s) for providing base layer data objects to a computing device including entity and relationship base layer data objects. The server(s) may receive a conditional rule from a computing device which defines contextual relationship between two entity base layer data objects. The server(s) may determine that a first entity base layer data object and a second entity base layer data object satisfy the contextual relationship by applying the conditional rule received from the computing device to attributes corresponding to first and second entity base layer data objects. The server(s) may store an association between the first and second entity base layer data object in accordance with the contextual relationship.