Computing Component Interruption Prioritization With Bipartite Graphs
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Solution Overview
Problem
Complex technology infrastructural computing systems face significant challenges in efficiently and automatically resolving multiple hardware and software interruptions, which can have far-reaching and long-term impacts if not addressed promptly.
Innovation Solution
A system that generates a Bi-Partite knowledge graph to prioritize interruptions and potential response computing components, dynamically updating based on interruption and response factors to automatically select and resolve the highest priority interruptions using available resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple interruptions are manually sorted and resolved, then resolution accuracy can be maintained, but the process becomes time-consuming and difficult to prioritize
Solution Approach 1:
The system performs self-service by automatically analyzing interruptions and selecting resolution actions without manual intervention. The computer-implemented method autonomously processes interruption data, evaluates priority levels, and determines resolution steps, eliminating the need for manual sorting while maintaining accurate prioritization through algorithmic decision-making.
Solution Approach 2:
The patent replaces manual mechanical processes with automated computer-based systems. Instead of human operators manually sorting and prioritizing interruptions, the system uses computational algorithms to analyze interruption data, evaluate priorities, and select resolution actions, thereby reducing time consumption while maintaining precision through digital processing.
2Reliability
If manual input is used for interruption resolution, then control and accuracy are maintained, but computational resources and network load increase
Solution Approach 1:
The system performs self-service by automatically analyzing interruptions and selecting resolution actions without manual intervention. The computer-implemented method autonomously processes interruption data, evaluates priority levels, and determines resolution steps, eliminating the need for manual sorting while maintaining accurate prioritization through algorithmic decision-making.
Solution Approach 2:
The patent replaces manual mechanical processes with automated computer-based systems. Instead of human operators manually sorting and prioritizing interruptions, the system uses computational algorithms to analyze interruption data, evaluate priorities, and select resolution actions, thereby reducing time consumption while maintaining precision through digital processing.
3Productivity
If interruptions are resolved quickly, then system impact is minimized, but complex prioritization becomes difficult to manage
Solution Approach 1:
The system segments the complex prioritization management into distinct modular components: interruption detection, priority evaluation, and resolution execution. Each component handles a specific aspect of the process independently, reducing overall complexity while enabling rapid resolution through streamlined workflows.
Solution Approach 2:
The patent replaces manual mechanical processes with automated computer-based systems. Instead of human operators manually sorting and prioritizing interruptions, the system uses computational algorithms to analyze interruption data, evaluate priorities, and select resolution actions, thereby reducing time consumption while maintaining precision through digital processing.
Data Source
AI summary
Systems, computer program products, and methods are described herein for determining and resolving interruptions to computing components. The present disclosure is configured to identify an interruption(s) and a potential response computing component(s); generate a Bi-Partite knowledge graph comprising the interruption(s) within a interruption priority queue(s) and the potential response computing component(s) within a response priority queue(s), wherein the Bi-Parte knowledge graph comprises a relational edge between the interruption priority queue(s) and the response priority queue(s); dynamically update the Bi-Partite knowledge graph with the interruption priority queue(s) based on an interruption factor(s) and the response priority queue(s) based on potential response factor(s); select, based on the updated interruption priority queue(s) and the updated response priority queue(s), a priority interruption and a priority potential response computing component; and resolve the priority interruption with the priority response computing component.


