Graph-Based Line Configuration Queries for Product Lifecycle Data
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Solution Overview
Problem
Conventional product life cycle management systems face significant computational challenges in managing complex manufacturing line configurations for customizable products due to the large size and variability of line configuration data models, leading to inefficiencies in querying and data processing.
Innovation Solution
A product life cycle management system utilizing a graph data structure with connected nodes for applicability groups and object nodes, allowing for efficient manufacturing-related queries by determining object-applicability group relationships, thereby improving query performance and reducing computational intensity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If relational data storage models are used to store line configuration data, then the system can maintain data integrity and relationships, but query performance becomes extremely slow and computationally intensive as the target table grows to millions of records
Solution Approach 1:
The patent segments the monolithic relational target table into a graph data structure with multiple interconnected tables (node table, edge table, property table). This segmentation allows queries to traverse only relevant portions of the data through graph traversal algorithms, avoiding full table scans and dramatically improving query performance while maintaining data integrity through the graph's inherent relationship structure.
Solution Approach 2:
The patent creates a graph-based copy of the relational data model, where nodes represent entities and edges represent relationships. This graph copy enables efficient traversal and filtering operations by physically representing relationships as connectable structures, allowing the system to answer complex queries without scanning the entire relational target table.
2Adaptability or versatility
If the target table grows to accommodate millions of components and thousands of variations, then the system can represent all product configurations, but the complexity of search processes grows exponentially making operations unfeasible
Solution Approach 1:
The patent implements dynamic filtering and traversal capabilities in the graph data structure, where queries can dynamically navigate through connected nodes and edges based on specified criteria. This dynamic approach allows the system to handle millions of components and thousands of variations by only traversing relevant paths in the graph, rather than performing static full-table scans, thus managing complexity efficiently.
Solution Approach 2:
The graph data structure acts as an intermediary between the raw relational data and the query processing logic. By introducing graph nodes and edges as intermediate representations, the system transforms complex relational joins into simpler graph traversal operations, reducing search process complexity while maintaining comprehensive product configuration coverage.
Data Source
AI summary
A product life cycle management system for use in manufacturing a customizable manufactured article using one of a plurality of manufacturing line configurations is described. A graph data structure is stored in memory, including a plurality of connected nodes including one or more applicability group nodes for applicability groups representing the manufacturing line configurations and object nodes for objects representing components used in the line configurations, the object nodes being connected to the one or more corresponding applicability group nodes to thereby encode in the graph data structure the manufacturing line configurations with which each object is related. A manufacturing-related query from a user is performed on the graph data structure, and one or more object-applicability group relationships are determined based on the manufacturing-related query.