Executable Graph Sub-Graph Processing for Low-Latency Execution
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Solution Overview
Problem
Existing n-tier architectures separate data storage from processing logic, leading to impedance mismatches and inhibiting flexibility, extensibility, and responsiveness, particularly in time-critical systems.
Innovation Solution
Executable graph-based models are decomposed into sub-graphs, integrating data and processing logic within a single model, allowing dynamic execution and separation when at rest, reducing processing delays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If data storage is separated from processing logic in n-tier architecture, then system modularity and maintainability are improved, but system flexibility, extensibility, and responsiveness deteriorate
Solution Approach 1:
The patent merges data storage and processing logic into a unified graph-based model where both coexist in the same computational space. The graph model simultaneously represents data entities/relationships and processing operations, eliminating the traditional separation between data layer and processing logic layer, thereby achieving both modularity and flexibility.
Solution Approach 2:
The graph-based model serves multiple functions: it acts as both the data structure (storing entities and relationships) and the processing logic (through executable operations on graph nodes and edges). This universal model handles both data representation and computational operations, improving system adaptability while maintaining structural organization.
2Stability of the object's composition
If data storage is separated from processing logic in n-tier architecture, then clear separation of concerns is achieved, but processing delays increase due to impedance mismatch
Solution Approach 1:
By combining data and processing logic in the graph model, the patent eliminates the impedance mismatch that causes processing delays. Operations can directly access and manipulate graph data structures without requiring data transfer between separate storage and processing layers, significantly reducing latency in time-critical applications.
3Speed
If entire graph-based model is loaded into memory for execution, then processing speed is improved, but memory resource consumption increases
Solution Approach 1:
The patent segments the graph-based model into independent sub-graphs that can be selectively loaded into memory based on execution needs. Each sub-graph represents a modular unit of computation that can be loaded, executed, and unloaded independently, allowing the system to maintain high processing speed for active sub-graphs while minimizing overall memory consumption through selective loading.
Solution Approach 2:
The system dynamically loads and unloads sub-graphs based on real-time execution requirements. Rather than statically loading the entire model, the system adapts memory usage by loading only the necessary sub-graphs into memory when needed and persisting others, optimizing the balance between processing speed and memory resource consumption.
Data Source
AI summary
A method for dynamic execution of sub-graphs within executable graph-based models is provided. Processing circuitry obtains an executable graph-based model comprising a plurality of sub-graphs and an overlay structure comprising processing logic associated with the plurality of sub-graphs. Each sub-graph defines a hierarchical structure of related nodes. The processing circuitry receives a stimulus and a context associated with the stimulus. In response to the stimulus being received and based on the context, the processing circuitry maps the stimulus to a first sub-graph of the executable graph-based model. The processing circuitry causes execution of processing logic within the overlay structure based on the mapping. The processing logic is associated with one or more nodes of the first sub-graph.