Graph-Based Model History Overlay for Low-Latency Node Retrieval
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Solution Overview
Problem
Conventional approaches to maintaining historical data in digitized and automated systems lead to increased latency during real-time operations due to the need for external storage and retrieval of historical data, which is critical for tasks like trend analysis and anomaly detection.
Innovation Solution
The implementation of in-situ history in executable graph-based models, where historical data is generated and maintained within the system using history overlay nodes, allowing for reduced latency by eliminating the need for external data retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If historical data is stored externally in conventional systems, then data storage is achieved, but latency increases during real-time operations due to external retrieval requirements
Solution Approach 1:
The patent merges historical data storage and processing logic directly into the graph-based model nodes themselves, eliminating the separation between operational data and historical data. This integration allows the system to access historical data in-situ during real-time operations without external retrieval, thereby reducing latency while maintaining a unified system architecture rather than adding complex external storage layers
2Loss of time
If historical data is maintained outside the executable model, then data integrity is preserved, but time complexity increases during data retrieval operations
Solution Approach 1:
The graph-based model nodes perform self-service by maintaining their own historical versions internally through version control mechanisms. Each node can independently access its historical states without requiring external data retrieval operations, enabling the system to reduce time complexity while improving data accessibility during real-time operations
3Productivity
If conventional data storage methods are used, then data retention is achieved, but operational efficiency decreases due to external retrieval requirements
Solution Approach 1:
The patent implements a nested structure where historical data versions are embedded within the graph-based model nodes themselves. Each node contains nested historical versions of its state, allowing the system to retrieve historical information efficiently during operations without sacrificing productivity, as the historical data is already embedded within the operational structure rather than requiring external access
Data Source
AI summary
An overlay system is provided that includes a storage element and processing circuitry. The storage element stores an executable graph-based model that includes various active nodes, various history nodes, and various history overlay nodes. Each active node is associated with one or more history nodes and one or more history overlay nodes. The one or more history overlay nodes facilitate the generation and maintenance of one or more history nodes (e.g., one or more historical versions of the corresponding active node). The processing circuitry receives a contextualized stimulus associated with the overlay system and identifies an active node and one or more associated history nodes, in the executable graph-based model based on the context. The processing circuitry further executes an operation associated with the stimulus based on the identified active node, the associated one or more history overlay nodes, and the identified one or more history nodes.