Graph Database for Merchant Cardholder Relationship Detection
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Solution Overview
Problem
Relational databases are inefficient in detecting interrelationships between large datasets of merchants and cardholders, as they store data in tabular form, making it cumbersome to identify connections beyond a few degrees from a source merchant or cardholder, and require complex join operations that degrade system performance.
Innovation Solution
Converting transaction data from relational databases into a graph database format, where nodes represent entities and edges represent relationships, allowing for efficient identification and generation of new relationships between merchants and cardholders based on common transaction data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If transaction data is stored in relational database tables, then data can be organized in tabular form with unique identifiers, but it becomes cumbersome and inefficient to detect interrelationships between merchants and cardholders across multiple tables
Solution Approach 1:
The patent transforms the traditional tabular data structure into a graph data structure, representing entities (merchants, cardholders) as nodes and relationships as edges. This dimensional change from tables to graphs enables efficient relationship detection through graph traversal algorithms, eliminating the need for complex multi-table join operations while maintaining data organization and accessibility.
2Loss of information
If multiple tables are joined to extract useful information from large datasets, then relationships between entities can be identified, but system performance degrades with millions or billions of records
Solution Approach 1:
The patent pre-computes and stores relationships between entities as edges in the graph structure during data ingestion. This preliminary action of establishing relationships upfront allows for efficient query execution later, as the system can directly traverse pre-defined edges rather than performing complex join operations on raw transaction data when queries are executed.
3Ease of manufacture
If transaction data is stored in disconnected tabular form, then data storage is simple, but tremendous information and insight is lost about connections between entities
Solution Approach 1:
The patent merges the storage of entity data and relationship data into a unified graph structure. Instead of storing transactions, merchants, and cardholders in separate disconnected tables, the system combines them into an integrated graph where nodes represent entities and edges represent relationships, preserving connection information while maintaining storage efficiency.
Data Source
AI summary
A method of generating graph data components stored in a graph database is provided. The method is implemented using a graph component generator computing device in communication with a memory. The method comprises receiving transaction data from a relational database, wherein transaction data is derived from transactions, processed by a payment processor, between a first merchant of a plurality of merchants and a first cardholder of a plurality of cardholders, defining a graph including creating at least a first node representing at least the first merchant and a second node representing the first cardholder, identifying at least one relationship between at least two nodes including the first node and the second node in the graph based, at least in part, on transaction data common to the at least two nodes, and generating at least one edge between the at least two nodes, representing a new relationship between the at least two nodes.