Executable Hypergraph Inference Engine for Latency Reduction
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Solution Overview
Problem
Traditional n-tier architectures separate data storage from processing logic, leading to impedance mismatches and reduced flexibility, extensibility, and responsiveness, which hinders the performance of systems in time-critical applications.
Innovation Solution
The implementation of executable graph-based models that dynamically combine data and processing functionality at runtime, using an inference overlay structure to integrate inference engines and processing logic within the data model, allowing for efficient, configurable, and flexible data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional n-tier architecture separates data storage from processing logic, then data management and processing can be performed independently, but processing latency increases and system responsiveness deteriorates
Solution Approach 1:
The patent combines data storage and processing logic into a unified graph-based model where data nodes and processing functions coexist in the same computational space. This eliminates the traditional separation between data layer and processing layer, allowing immediate access to data during processing operations and significantly reducing processing latency while maintaining manageable complexity through the graph structure.
2Adaptability or versatility
If data and processing logic are separated in traditional architecture, then each layer can be optimized independently, but system flexibility and extensibility are reduced
Solution Approach 1:
The patent segments the system into discrete graph nodes representing data entities, relationships, and processing functions. Each node can be independently defined, modified, and extended while maintaining its connections within the overall graph structure. This segmentation enables high flexibility and ease of operation as individual components can be manipulated without affecting the entire system.
Solution Approach 2:
The graph-based model provides a dynamic structure where data and processing logic can be adaptively reconfigured at runtime. The system allows for dynamic addition, removal, and modification of graph nodes and edges, enabling the system to adapt to changing requirements while maintaining operational simplicity through the unified graph interface.
3Productivity
If data storage and processing are separated, then data can be stored efficiently, but data access and processing efficiency are reduced
Solution Approach 1:
By merging data storage and processing logic into the same graph-based computational model, the system eliminates impedance mismatch between data representation and processing operations. Data nodes can be directly accessed and manipulated by processing functions without requiring data conversion or layer traversal, significantly improving data processing efficiency while preserving complete information integrity.
Data Source
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AI summary
An executable inference hypergraph representing a rule-based model, the executable inference hypergraph comprising a first hyperedge associated with a first inference rule of the rule-based model and encapsulating a plurality of value nodes storing a plurality of values such that the plurality of values form a part of a set of terms used to evaluate the first inference rule, wherein the plurality of value nodes include at least one of the one or more value nodes of a graph-based model. The executable inference hypergraph further comprising a rule overlay node coupled to the first hyperedge thereby forming a first executable inference rule, wherein the rule overlay node comprises processing logic operable to evaluate the first inference rule using the set of terms encapsulated by the first hyperedge. The executable inference hypergraph is executed to determine an inference outcome.