Dynamic Entity Activity Graphs for Incomplete Relationship Data
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Solution Overview
Problem
Existing systems struggle with static and out-of-date analysis of entity behaviors, relying on single business record sources, which hinders holistic and dynamic representation of entity relationships and activities, especially when data is incomplete or inconsistent across multiple entities.
Innovation Solution
A computer-based method and system that generates a dynamic entity activity graph by aggregating entity-specific and activity-related data records, using machine learning and graph analysis to create a dynamic network model that updates in real-time, linking entities based on their activities and behaviors, even when incomplete data is present.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If single business record sources are used for entity analysis, then data processing simplicity is maintained, but the completeness and accuracy of entity relationship representation deteriorates
Solution Approach 1:
The patent combines multiple business record sources into a unified data structure that integrates entity-specific data, entity-related activity data, and relationship information. This merging allows comprehensive entity relationship representation while maintaining manageable processing through standardized schemas and centralized storage architecture.
2Productivity
If static entity evaluation is performed, then processing speed is maintained, but the currency and relevance of entity relationship data deteriorates
Solution Approach 1:
The patent implements a dynamic data structure where entity relationships and activities are continuously updated as new business records are processed. The system maintains current entity evaluations by automatically incorporating new activity data and relationship changes, ensuring data currency without requiring complete reprocessing of historical records.
3Use of energy by moving object
If individual entity evaluation is performed, then computational resources per entity are optimized, but the holistic understanding of entity groups deteriorates
Solution Approach 1:
The patent adds a group-level dimension to individual entity evaluation by organizing entities into hierarchical structures and communities. The system simultaneously maintains individual entity profiles and group-level aggregations, enabling both detailed individual analysis and holistic group understanding through multi-level data organization and analysis capabilities.
Data Source
AI summary
In order to facilitate dynamic graphing of entity networks based on activity, systems and methods include a processor receiving entity-specific data records and a plurality of entity-related activity records for a plurality of entities, where each entity-specific activity record includes activity data regarding at least one activity associated with an entity. The processor generates graph nodes for an entity activity graph based on the plurality of entity-specific data records, where each graph node of the plurality of graph nodes represents the particular entity and then generating an activity data structure, including the graph nodes and edges between the graph nodes, where the edges represent characteristics of the activities between graph nodes based on the entity-related activity record.