Graph Database Relationship Identification via Compact Indexing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Analyzing large data graphs to determine relationships between individuals is time-consuming due to the vast amount of data involved, requiring significant processing time to traverse and identify connections.
Innovation Solution
A method and system that create a relationship database with compact data structures, using a memory index to efficiently store and retrieve relationship data, allowing for quick identification of relationships between individuals by accessing and comparing relationship records associated with given persons of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If one traverses a large data graph to determine relationships between two people, then the relationship can be identified, but the processing time and computational resources required become excessively large
Solution Approach 1:
The system pre-processes and stores relationship data in a structured format before actual queries are made. Relationship records are pre-indexed and organized in the data graph, allowing rapid retrieval during relationship determination without needing to traverse the entire graph from scratch.
Solution Approach 2:
The invention extracts only the necessary relationship data needed to determine connections between specific individuals. By using targeted queries and filtering mechanisms, the system retrieves only relevant relationship records rather than processing the entire large data graph, significantly reducing computational overhead.
2Loss of information
If one accesses all nodes connected to initial nodes to find common links, then complete relationship information can be obtained, but the amount of data to be processed becomes overwhelming
Solution Approach 1:
The system applies different levels of detail and data granularity to different parts of the relationship graph. For direct relationships, detailed information is stored and accessed; for indirect relationships, summarized or aggregated data is used. This allows comprehensive relationship analysis while reducing the effective data volume processed.
Solution Approach 2:
The relationship data is segmented into discrete, manageable records that can be independently processed and compared. Each relationship record contains specific attributes (person identifiers, relationship types, confidence levels) that can be selectively accessed and processed, avoiding the need to handle the entire graph as a monolithic data structure.
3Loss of information
If a data graph stores comprehensive information about individuals and their relationships, then detailed analysis is possible, but the complexity of analyzing and traversing the graph increases
Solution Approach 1:
The invention introduces intermediary data structures and indexing mechanisms that mediate between the comprehensive relationship data and the query processing operations. These intermediaries (such as relationship records, indexes, and pre-processed data structures) simplify the traversal and comparison processes while maintaining access to the full comprehensive data when needed.
Data Source
AI summary
A graph database system is used to determine relationships between persons. The graph database system manages relationship data records having a compact data structure stored at a relationship database and accessed to find links or common relationships. For determining relationships between two people, the relationship records in the relationship paths for those two people are alternatively accessed and compared it to each other. Relationship records may include an identifier for a person having a relationship to a given individual, a relationship type, and a weight reflecting the confidence level or quality of the relationship.