Property Graph Schema Optimization for Query Response Time
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Solution Overview
Problem
Existing property graph-based query systems face challenges in optimizing schema design, leading to suboptimal query response times in high-performance applications due to inefficient edge traversals and storage constraints.
Innovation Solution
An ontology-driven approach is employed to generate an optimized property graph schema by prioritizing relationships based on centrality analysis and cost-benefit models, modifying inheritance, union, 1:1, 1:M, and M:N relationships to reduce edge traversals and adhere to storage space limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If property graph schema is optimized by applying relationship types and modifying inheritance, union, 1:1, 1:M, and M:N relationships, then query response time is improved, but storage space consumption increases
Solution Approach 1:
The patent changes the structural parameters of the property graph schema by modifying relationship types (inheritance, union, 1:1, 1:M, M:N) and applying optimization rules that reorganize how data is stored and accessed. This restructuring reduces edge traversals during queries while managing storage space through selective optimization based on centrality analysis and cost-benefit models.
Solution Approach 2:
The patent performs preliminary optimization of the property graph schema before queries are executed. By pre-analyzing centrality scores and cost-benefit ratios of different relationships, the system prepares an optimized schema structure in advance that reduces query response time without incurring storage overhead during actual query operations.
2Productivity
If ontology-driven approach is used to generate optimized property graph schema, then query performance is improved, but system complexity increases
Solution Approach 1:
The patent implements self-service mechanisms where the system automatically performs centrality analysis, cost-benefit evaluation, and schema optimization without requiring manual intervention. The ontology-driven approach enables the system to self-optimize by automatically identifying critical relationships and applying appropriate transformation rules based on predefined criteria.
Solution Approach 2:
The patent incorporates feedback loops where query patterns and performance metrics are continuously analyzed to refine the property graph schema optimization. The system uses centrality scores and cost-benefit models as feedback mechanisms to iteratively improve the schema structure, balancing query performance with storage constraints.
Data Source
AI summary
According to one or more embodiments of the present invention, a computer-implemented method for providing a query response includes receiving, by a computing device, a domain-specific knowledge graph. The method further includes generating a first property graph schema, a property graph schema includes vertices, edges, and properties of the domain-specific knowledge graph, wherein the first property graph schema is generated based on an ontology of the domain-specific knowledge graph. The method further includes generating a second property graph schema from a copy of the first property graph schema that is optimized by applying one or more types of relationships in the first property graph schema. The method further includes instantiating a property graph using the second property graph schema. The method further includes receiving a query to obtain particular data from the domain-specific knowledge graph. The method further includes responding to the query using the property graph.