Graph Data Structure Construction for Fragmented Entity Data
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Solution Overview
Problem
Existing systems struggle to effectively utilize fragmented and inconsistently named raw data to build a coherent graph data structure that represents relationships between entities, such as organizations and their associated applications, contacts, and interactions, which is crucial for providing enhanced services and products.
Innovation Solution
A method and system for building a graph data structure by determining vertices and edges from raw data, inferring relationships, and using builder modules to incrementally update the graph, ensuring freshness and accuracy of data through event-driven or batch processing, and utilizing a graph interface module for writing and reading data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data storage and processing methods are used for fragmented raw data, then data consistency and relationship tracking become difficult, but implementing a graph data structure requires complex data processing and integration mechanisms
Solution Approach 1:
The system segments the graph data structure into distinct components: vertices representing entities and edges representing relationships. Builder modules independently process and populate these segments, allowing complex data to be handled in manageable units while maintaining overall data consistency through the structured graph framework.
Solution Approach 2:
The patent introduces intermediary builder modules that act as mediators between raw data sources and the graph data structure. These modules transform and validate data before insertion, handling the complexity of data integration while presenting a simplified interface to the rest of the system and ensuring data consistency.
2Adaptability or versatility
If a comprehensive graph data structure is built to represent all entity relationships, then enhanced services and products can be provided, but data freshness and accuracy become difficult to maintain
Solution Approach 1:
The system implements feedback mechanisms where the graph data structure continuously receives updates from builder modules that monitor changes in underlying data sources. This feedback loop ensures data freshness by automatically detecting and propagating changes throughout the graph, maintaining accuracy while supporting comprehensive relationship tracking for enhanced services.
Solution Approach 2:
The builder modules perform preliminary validation and processing of data before it is inserted into the graph structure. This preliminary action ensures data accuracy at the point of entry, reducing the need for later corrections and maintaining data freshness without requiring continuous comprehensive validation of the entire graph.
3Productivity
If incremental updates are implemented to maintain graph data freshness, then processing time increases, but batch processing improves data consistency
Solution Approach 1:
The system dynamically adjusts its processing mode based on requirements. Builder modules can operate in incremental update mode for real-time responsiveness or switch to batch processing mode for comprehensive data consistency. This dynamic flexibility allows the system to optimize between productivity and reliability depending on the specific operational context and data requirements.
Data Source
AI summary
Methods comprising determining a dataset of entries, each comprising an entity identifier and associated application identifier(s) are provided. One method comprises determining a set of application pairs from the dataset; determining a correlation score for each application pair based; responsive to the correlation score exceeding a threshold correlation value: determining application identifiers of the application pair as being correlated; providing a correlation write request comprising the applications identifiers of the pair to an app recommender database.