Graph Search Using Index Vertices for Query Optimization
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Solution Overview
Problem
Graph databases face inefficiencies in querying and processing as the number of vertices and edges increases, particularly when multiple searchable attributes are involved, leading to increased processing time and resource consumption.
Innovation Solution
The implementation of index vertices in a graph management system allows for efficient identification of relevant data instance vertices by using additional vertices to index multiple attributes, enabling faster query response through metadata and flag association.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If graph databases store data in vertices and edges with multiple searchable attributes, then data modeling capability and query flexibility are improved, but query processing time and resource consumption increase
Solution Approach 1:
The patent segments the graph database by introducing a separate index structure that divides vertices into index vertex groups based on attribute values. This segmentation allows queries to target specific groups rather than traversing the entire graph, reducing query processing time while maintaining the ability to search by multiple attributes.
Solution Approach 2:
The patent introduces index vertices as intermediary elements between the query system and the actual data vertices. These index vertices store attribute information and serve as mediators that guide query traversal, eliminating the need to examine every vertex's attributes directly and thus reducing processing time.
2Quantity of substance
If the number of vertices and edges in a graph increases, then data representation capability is improved, but query efficiency deteriorates
Solution Approach 1:
The patent applies segmentation by organizing vertices into distinct index vertex groups based on their attribute values. This allows the system to handle large numbers of vertices efficiently by dividing the search space into manageable segments, maintaining query efficiency even as the graph scales to accommodate more data.
Solution Approach 2:
The patent implements preliminary action by pre-organizing vertices into index vertex groups based on their attributes before queries are executed. This pre-processing creates an indexed structure that enables rapid query response without requiring full graph traversal, thus maintaining query efficiency as the vertex count increases.
3Measurement precision
If full graph traversal is performed to identify vertices with specific attributes, then query accuracy is improved, but processing overhead increases
Solution Approach 1:
The patent extracts attribute information from vertices and stores it in separate index vertex groups. This extraction allows the system to identify matching vertices by querying the index structure rather than examining every vertex's attributes, maintaining query accuracy while significantly reducing processing overhead.
Solution Approach 2:
The patent uses index vertices as intermediaries that store and organize attribute data. Queries first interact with these intermediary index vertices to identify candidate matches, then verify against actual vertices only if necessary. This two-stage approach maintains query accuracy while reducing the computational work required.
Data Source
AI summary
Systems, methods, and software described herein provide graph searching via index vertices. In one implementation, a graph management system may receive a request to identify vertices of a first vertex type in a graph, wherein the request defines one or more attributes associated with the vertices. In response to the request, the graph management system may identify one or more index vertices associated with the one or more attributes, wherein the one or more index vertices is associated with one or more second vertex types. The graph management system may further identify the vertices in the graph with edges that couple to the one or more index vertices.