Graph Data Structure for AI Relationship Identification

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Solution Overview

Problem

Traditional relational database management systems face performance issues due to the need for complex join and union operations to identify relationships between entities, which consume processing cycles and reduce system performance.

Innovation Solution

A computer-implemented method using artificial intelligence to represent and store data in a graph data structure by identifying relationships between business requirements and workflow data, generating a graph data structure, and refining the adjacency matrix based on trends, thereby reducing the need for complex operations and improving data retrieval efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If complex join and union operations are performed to identify relationships between entities in a relational database, then the relationships can be identified, but the processing cycles are consumed and system performance is reduced

Engineering Contradiction:

Improverelationship identification accuracyVSAvoiddata retrieval speed

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent pre-computes and stores the relationships between entities in a graph data structure during data ingestion, rather than computing them on-demand during queries. This preliminary action transforms the complex join operations into simple graph traversals, resolving the contradiction between accurate relationship identification and fast retrieval speed

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces the traditional relational database mechanics (join operations on tables) with graph database mechanics (traversal on nodes and edges). This substitution eliminates the need for complex join operations while maintaining relationship identification accuracy, thereby improving data retrieval performance

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Ease of manufacture

If data is stored in a traditional relational database structure, then data can be organized in tables, but complex operations are required to traverse and retrieve related data

Engineering Contradiction:

Improvedata organization simplicityVSAvoidoperation complexity

Core Design Contradiction:

Ease of manufactureVSDevice complexity

Solution Approach 1:

The patent transitions from the two-dimensional table structure of relational databases to the multi-dimensional graph structure with nodes, edges, and hierarchical relationships. This dimensional change allows data to be organized in a more natural way that reflects real-world relationships, reducing the complexity of operations needed to traverse and retrieve related data

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Data Source

PatentUS11520828B2Methods for representing and storing data in a graph data structure using artificial intelligence

Publication Date: 2022.12.06 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

A computer-implemented method for representing and storing data in an extendable graph data structure using artificial intelligence includes obtaining business requirements data, business workflow data, and dictionary data. A relationship between one or more entities in the obtained business requirement data and the business workflow data is identified using the obtained dictionary data and applying a natural language processing technique and topic based trend identification from existing entities and associated attributes. An extendable graph data structure represented by an enriched adjacency matrix for the identified relationship between the one or more entities is generated and stored. One or more trends are identified using the stored graph data structure.