Entity Graph Node Linkage for Distributed File Systems
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Solution Overview
Problem
Current systems face challenges in efficiently displaying and understanding relationships between nodes in a network, requiring significant processing power and struggling to present node relationships effectively, especially when dealing with large datasets and complex relationships between entities such as individuals and corporations across different countries.
Innovation Solution
A system that generates dynamic customized user interfaces by storing entity attributes in a distributed file system, calculating similarities between entities, and displaying entity graphs with edges representing relationships, allowing users to quickly identify and visualize customer-to-customer, business-to-business, and business-to-customer relationships, with features like confidence levels and behavioral matches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If nodes are displayed individually in isolation, then each node appears as a unique entity, but relationships between nodes become difficult or impossible to understand
Solution Approach 1:
The system segments the network into individual node displays while maintaining relationship context through entity graphs. Each node can be examined individually for its attributes, while the graph structure simultaneously presents relationships between nodes, resolving the contradiction between individual node analysis and relationship understanding.
Solution Approach 2:
The interface nests multiple levels of information within a unified display. Entity graphs contain nodes that represent individual entities, while edges represent relationships between these nodes. This nested structure allows detailed node information and relationship information to coexist without overwhelming the user.
2Productivity
If current systems are used to present node relationships, then processing power is consumed, but the effectiveness of the network is limited
Solution Approach 1:
The system dynamically generates and updates entity graphs based on user interactions and data changes. The graphs adapt to show relevant relationships and can be customized based on user needs, improving network effectiveness while optimizing processing by only computing and displaying necessary relationship information rather than all possible relationships.
Solution Approach 2:
The system presents relationship information locally around relevant nodes rather than globally across the entire network. Entity graphs focus on and highlight relationships pertinent to specific nodes or queries, reducing the processing power required to present relationships while maintaining network effectiveness for the user's specific needs.
3Quantity of substance
If nodes representing different entities are maintained without understanding relationships, then network scale is increased, but processing requirements increase and effectiveness decreases
Solution Approach 1:
The system pre-establishes and maintains relationship metadata between nodes in a structured format. Relationship information is prepared and organized in advance, allowing quick retrieval and display when needed. This preliminary organization of relationship data reduces processing time when queries are made, even as the number of nodes increases.
Solution Approach 2:
The system changes the parameter of relationship representation from unstructured data to structured entity graph format. By transforming relationship information into a standardized graph structure with nodes and edges, the system enables efficient processing and display of relationships among large numbers of nodes, reducing processing time while maintaining scalability.
Data Source
AI summary
A distributed file system may store a plurality of entity attributes. A node linking system may identify similarities between entity attributes for different entities. The node linking system may generate an entity graph which displays relationships and similarities between entities in a graphical user interface.