Multi-Graph Search and Merge Engine for Unified Pattern Analysis
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Solution Overview
Problem
Current systems lack the ability to efficiently search and amalgamate multiple graphs for common elements and patterns, limiting their analytical value and usability in displaying and analyzing complex relationships.
Innovation Solution
A multi-graph search and merge engine that receives search criteria via a user interface, identifies matching nodes or subgraphs across multiple graphs, and merges them into a single, unified graph for display, while retaining distinct identities and relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple graphs are searched and merged into a single unified graph, then analytical value and pattern identification capability are improved, but system complexity and computational resources required are increased
Solution Approach 1:
The patent merges multiple graphs into a single unified graph by identifying common nodes and relationships across graphs, then combining them while preserving distinct identities. This allows the system to maintain comprehensive analytical value from multiple data sources while presenting a consolidated view that reduces information loss and improves pattern identification capability.
Solution Approach 2:
The unified graph serves multiple functions simultaneously: it stores data from multiple source graphs, enables cross-graph pattern matching, supports various search criteria (node profiles, subgraph profiles), and provides a consolidated analytical view. This multi-functionality improves analytical value without requiring separate systems for each graph operation.
2Loss of information
If multiple graphs are searched and merged into a single unified graph, then analytical value and pattern identification capability are improved, but computational resources and processing time are increased
Solution Approach 1:
The system performs preliminary actions by pre-processing graphs to identify and mark common nodes and relationships before the actual merge operation. Search criteria are prepared in advance, and the system can quickly match new graphs against previously processed patterns, reducing processing time during actual search and merge operations while maintaining comprehensive analytical value.
3Measurement precision
If graphs are merged by superimposing them onto each other aligned at matching nodes, then unified pattern recognition is improved, but complexity of managing distinct graph identities is increased
Solution Approach 1:
The patent applies local quality by allowing different parts of the merged graph to have different properties. Matching nodes serve as alignment points with specific characteristics, while other nodes and edges retain their original graph's properties and identities. This enables precise pattern recognition at matching points while preserving the distinct characteristics of each source graph throughout the rest of the structure.
Solution Approach 2:
The system creates copies of graph data during the merge process, maintaining multiple representations of the same information. When graphs are superimposed at matching nodes, the system copies node and edge data while preserving source graph identifiers and relationships. This allows accurate pattern recognition through alignment while managing identity complexity through replicated data structures that track original sources.
Data Source
AI summary
Aspects of the disclosure relate to a system for amalgamating a plurality of graphs. The system may include a set of graphs. A user may input search criteria via a user interface (“UI”) module. The system may search the set of graphs for a subset of qualifying graphs that satisfy the search criteria. The subset of graphs may be merged into an amalgamated graph. Merging the graphs may include superimposing the qualifying graphs over each other at a locus. The locus may be a node or a sub-graph. The amalgamated graph may be displayed via the UI module.