Graph Data Search Using Reduction Subgraphs
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Solution Overview
Problem
Current graph data search methods consume significant time and storage resources due to the need for maintaining large graph data samples, leading to resource wastage and inefficient query processes.
Innovation Solution
A method that filters out non-relevant graph nodes based on query conditions and available resource constraints to create a reduction subgraph, allowing for efficient querying within this subgraph, reducing storage overhead and improving search accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full big data analysis is performed to obtain precise search results, then search accuracy is improved, but time resources and storage resources are significantly consumed
Solution Approach 1:
The patent extracts only the necessary parts of the graph data by filtering out nodes and edges that do not meet the query conditions. A reduction subgraph is constructed containing only relevant nodes and edges, which significantly reduces the data volume for analysis while maintaining search accuracy for the queried information.
Solution Approach 2:
The patent segments the graph data into a reduction subgraph that is processed separately from the full graph data. By dividing the large graph data set into a manageable subgraph based on query conditions, the system can perform analysis more efficiently without sacrificing the accuracy of search results.
2Measurement precision
If full big data analysis is performed to obtain precise search results, then search accuracy is improved, but storage resources are significantly consumed
Solution Approach 1:
The patent extracts only the necessary parts of the graph data by filtering out nodes and edges that do not meet the query conditions. A reduction subgraph is constructed containing only relevant nodes and edges, which significantly reduces the data volume for analysis while maintaining search accuracy for the queried information.
Solution Approach 2:
The patent segments the graph data into a reduction subgraph that is processed separately from the full graph data. By dividing the large graph data set into a manageable subgraph based on query conditions, the system can perform analysis more efficiently without sacrificing the accuracy of search results.
3Loss of time
If data sampling-based query technology is used to reduce resource consumption, then time resources and storage resources are reduced, but additional storage overheads are needed to maintain graph data samples
Solution Approach 1:
The patent dynamically constructs the reduction subgraph based on the specific query conditions rather than maintaining a static sampled graph data structure. This dynamic approach allows the system to adapt the data structure to each query, eliminating the need for additional storage overheads to maintain fixed sampling structures while still achieving resource efficiency.
Solution Approach 2:
Instead of maintaining a pre-sampled graph data structure that requires additional storage, the patent extracts and processes only the necessary graph data on-demand based on query conditions. This eliminates the storage overhead associated with maintaining sampling structures while achieving resource efficiency through selective data processing.
Data Source
AI summary
A graph data search method and apparatus, where the method includes obtaining a query request including a query condition that carries a start graph node, the query request queries a first to-be-queried graph node matching the query condition from a graph data set, and the graph data set includes the start graph node, a plurality of to-be-queried graph nodes, an association relationship between the start graph node and the plurality of graph nodes, and an association relationship between each to-be-queried graph node and another graph node, filtering out, according to the query condition and a preset available resource condition, a second to-be-queried graph node that does not meet the query condition and an association relationship in the graph data set that includes the second to-be-queried graph node, and performing a query in the reduction subgraph using the query condition.