Fractal Graph Nodes for Faster Loading of Executable Subgraphs
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Solution Overview
Problem
Conventional graph-based models require frequent retrieval and reloading of nodes, leading to time-intensity, cost-intensity, and complexity due to their uniform granularity and lack of isolation for complex connections, impacting system performance and security.
Innovation Solution
The introduction of fractal nodes in an executable graph-based model, which are high-level nodes composed of bi-directional nodes connected by connection links, allowing for optimized loading and execution of processing logic directly on the fractal node, isolating complex interactions, and enhancing security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional graph-based models use uniform granularity nodes that are frequently retrieved and re-loaded, then complete control over each unit is achieved, but the system becomes time-intensive, cost-intensive, and complicated
Solution Approach 1:
The patent segments the graph-based model into fractal nodes that contain multiple bi-directional nodes and connection links. This segmentation allows the system to load and unload fractal nodes as single units rather than individual nodes, reducing the frequency and time of loading operations while maintaining control over the entire subsystem.
Solution Approach 2:
The patent implements a nested structure where fractal nodes contain multiple bi-directional nodes and connection links within them. This nesting allows hierarchical management of graph elements, enabling efficient loading of complex subgraphs as single fractal nodes while preserving the internal structure and relationships of contained nodes.
2Device complexity
If conventional graph-based models treat all nodes at the same granularity, then uniform control is achieved, but complex connections cannot be isolated leading to increased complexity
Solution Approach 1:
The patent segments complex connections into isolated fractal nodes that can be independently managed. Each fractal node encapsulates specific bi-directional nodes and their connection links, allowing complex subgraphs to be handled as discrete units rather than requiring management of individual connections across the entire graph.
Solution Approach 2:
The patent introduces fractal nodes as intermediary structures between simple nodes and complex connection patterns. These fractal nodes act as mediators that encapsulate complex bi-directional relationships, simplifying the overall graph structure while preserving the complexity of internal connections within each fractal node.
3Productivity
If conventional graph-based models frequently retrieve and reload nodes for optimized resource use, then resource utilization is improved, but the process becomes cost-intensive and inconvenient
Solution Approach 1:
The patent merges multiple bi-directional nodes and connection links into single fractal nodes. This consolidation allows the system to perform resource optimization by loading and unloading entire fractal nodes as single units, reducing the operational overhead and time costs associated with frequent node retrieval and reloading operations.
Solution Approach 2:
The nested structure of fractal nodes containing multiple bi-directional nodes enables efficient resource management. When a fractal node is loaded, all contained nodes become available simultaneously, eliminating the need for sequential loading of individual nodes and reducing the time and cost of resource optimization operations.
Data Source
AI summary
An overlay system is provided that includes a storage element and processing circuitry coupled thereto. The storage element stores an executable graph-based model having various fractal nodes and various connection links. The processing circuitry receives a stimulus and identifies a first fractal node based on the stimulus. The first fractal node includes various bi-directional nodes and connection links. Further, the processing circuitry determines a set of bi-directional nodes and a set of connection links from the first fractal node for processing the stimulus. The set of connection links couples the set of bi-directional nodes to each other. Additionally, the processing circuitry executes an operation associated with the stimulus based on the determined bi-directional nodes and connection links.