Graph Data Search Using Node Constraint Rules
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large-scale graph data searching becomes inefficient due to the need for comprehensive determination of each node matching search criteria, which increases search time.
Innovation Solution
A search apparatus that uses constraint data to determine whether the value of a first node matches the search criterion and predicts whether the value of a second node matches based on constraint rules, allowing for the omission of determinations on partial nodes without affecting the search result.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive determination of each node is performed to ensure accurate search results, then search result accuracy is maintained, but search time increases significantly
Solution Approach 1:
The system performs preliminary determination of constraint rules between nodes before executing the full search. By pre-establishing which node property determinations are necessary and which can be inferred, the system prepares the search framework in advance, avoiding redundant determinations during actual search execution while maintaining result accuracy.
Solution Approach 2:
The system performs determination of node properties partially rather than comprehensively. By using constraint rules to infer properties of some nodes from already-determined neighboring nodes, the system performs only the necessary minimum determinations required to guarantee accurate search results, significantly reducing the total number of determination operations.
2Productivity
If the number of determination operations is reduced to accelerate search, then search speed improves, but search result accuracy may be compromised
Solution Approach 1:
The system uses constraint rules as feedback mechanisms to verify whether partial determinations are sufficient. By continuously checking whether determined node properties satisfy the search criteria through constraint rule validation, the system ensures that reduced determination operations still produce accurate search results.
Solution Approach 2:
The system preliminarily analyzes constraint rules to identify which node determinations can be safely omitted. This preliminary analysis establishes a determination optimization strategy that guarantees search accuracy while minimizing the number of actual determination operations performed during search execution.
3Productivity
If constraint rules are used to predict node property matches, then the number of determination operations decreases, but system complexity increases
Solution Approach 1:
The system preliminarily establishes constraint rules between nodes before search execution. By pre-defining the relationships and dependencies between nodes in terms of constraint rules, the system transforms a complex runtime decision-making problem into a simpler predetermined framework, reducing the complexity of the actual search determination process.
Data Source
AI summary
A processor receives a search query for graph data representing a graph including nodes and edges, the search query specifying a search criterion. The processor determines whether a value of a first property associated with a first node matches the search criterion. The processor predicts whether a value of a second property associated with a second node matches the search criterion, in accordance with a result of the determining and a constraint rule between the first node and the second node. The processor generates a search result for the search query in accordance with the result of the determining and a result of the predicting.