Knowledge Graph Ranking for Computationally Efficient Data Execution

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Solution Overview

Problem

Existing data management systems face challenges in efficiently and effectively managing large, complex datasets due to the complexity of data querying instructions and data generating paths, leading to inefficiencies and potential data quality issues.

Innovation Solution

A method involving constructing knowledge graphs from data querying instructions and data generating paths, applying cost functions to evaluate and rank these objects based on similarities, efficiencies, and significances, and executing the highest-ranked objects to optimize data processing workflows.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If data querying instructions are used to manage large, complex datasets, then data retrieval capability is improved, but system computational efficiency deteriorates

Engineering Contradiction:

Improvedata retrieval capabilityVSAvoidcomputational efficiency

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The patent creates simplified copies of complex data querying instructions by generating knowledge graphs that represent the essential structure and relationships of the data. These knowledge graphs serve as lightweight alternatives to full SQL queries, enabling rapid data object identification and selection without the computational overhead of executing complex queries against large datasets.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The system performs preliminary actions by pre-generating knowledge graphs from data querying instructions and storing them alongside data objects. This advance preparation allows the system to quickly match and retrieve relevant data objects based on pre-computed knowledge representations, avoiding the need to parse and execute complex queries in real-time during data retrieval operations.

Inventive Principle:

Principle #10Preliminary action

2Reliability

If multiple data objects are evaluated and ranked using cost functions, then data quality is improved, but processing time increases

Engineering Contradiction:

Improvedata qualityVSAvoidprocessing time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent extracts only the most relevant features and characteristics of data objects by constructing knowledge graphs that capture essential relationships and structures. This selective extraction allows the cost function to evaluate data quality based on key attributes without processing the entire dataset, thereby maintaining high data quality standards while reducing evaluation time.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system changes the parameters of evaluation by transforming complex data objects into standardized knowledge graph representations with consistent structures. This parameter transformation enables efficient comparison and ranking through cost functions that operate on uniform data structures, reducing processing time while maintaining comprehensive data quality assessment.

Inventive Principle:

Principle #35Parameter changes

3Loss of information

If knowledge graphs are constructed from data querying instructions, then data object understanding is improved, but computational complexity increases

Engineering Contradiction:

Improvedata object understandingVSAvoidcomputational complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent creates simplified graphical representations (knowledge graphs) that copy the essential structure and relationships of data querying instructions without replicating the full computational complexity. These knowledge graphs preserve the semantic meaning and data relationships while using a more manageable graphical format that is easier to process and compare.

Inventive Principle:

Principle #26Copying

4Productivity

If data objects are ranked and selected based on cost scores, then computational efficiency is improved, but system complexity increases

Engineering Contradiction:

Improvecomputational efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent introduces knowledge graphs as intermediary structures that bridge the gap between complex data querying instructions and simple cost-based ranking. These knowledge graphs serve as a mediating representation that captures the essential characteristics needed for efficient ranking while avoiding the need to directly compare complex query structures, thereby improving computational efficiency without proportionally increasing system complexity.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20250298669A1Methods and systems for computationally efficient data object execution

Publication Date: 2025.09.25 ROYAL BANK OF CANADA

AI summary

Methods and systems for data management optimization are disclosed. The methods involve retrieving at least one of data querying instructions or data generating paths, and constructing knowledge graphs to visualize data structure within the data objects. Data objects are matched to determine similarities among the knowledge graphs. Cost functions determined by the similarities generate costs to evaluate aspects of the data objects, which are then ranked to inform data processing workflows within the data management system. This disclosure improves data handling efficiency and operational performance.