State Machine Knowledge Graph Computing System State Detection
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Solution Overview
Problem
It is challenging to accurately and efficiently predict when computing systems may start performing poorly or not at all, due to the complex interactions among numerous components.
Innovation Solution
The implementation of state machines and knowledge graphs in an electronic environment allows for the identification of a computing system's current state by analyzing data from various components and comparing it to pre-defined outputs and patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used to track computing system components, then implementation simplicity is maintained, but prediction accuracy of system performance deteriorates
Solution Approach 1:
The patent segments the computing system into multiple state machines, each representing a specific component or subsystem. Each state machine independently tracks the state of its associated component through state transitions based on observed events. This segmentation allows the system to manage complexity by dividing the overall monitoring task into smaller, manageable units while maintaining high prediction accuracy through comprehensive component-level tracking.
Solution Approach 2:
The patent introduces knowledge graphs as an intermediary layer that connects and relationships between multiple state machines. The knowledge graph stores and manages the complex interactions and dependencies between different system components, enabling the monitoring system to capture system-wide patterns without requiring direct complex interconnections between all state machines. This intermediary structure resolves the contradiction by providing a manageable way to represent complex relationships.
2Measurement precision
If comprehensive component interaction tracking is implemented, then prediction accuracy improves, but processing speed deteriorates
Solution Approach 1:
The patent implements dynamic state machines that adapt their behavior based on the current system state and observed patterns. Rather than continuously processing all possible interactions at full detail, the state machines dynamically adjust their monitoring intensity and transition evaluation based on current system conditions. This dynamic approach maintains high prediction accuracy by focusing computational resources on critical state transitions while improving processing speed by avoiding unnecessary computations during stable periods.
Solution Approach 2:
The patent pre-defines state transition rules and knowledge graph relationships before runtime monitoring begins. By establishing the framework of possible states, transitions, and component relationships in advance, the system avoids the need for complex real-time analysis of all interactions. During operation, the system only needs to evaluate whether observed events match predefined transition criteria, significantly improving processing speed while maintaining prediction accuracy through the comprehensive nature of the pre-defined model.
Data Source
AI summary
Systems, computer program products, and methods are described herein for determining a computing system's current state by implementing state machines and knowledge graphs in an electronic environment. The present disclosure is configured to identify at least one knowledge graph comprising a plurality of clusters associated with at least one computing system, each cluster comprises a plurality of state machines associated with the at least one computing system; identify data associated with the computing system; apply the data associated with the computing system to at least one state machine; generate at least one output associated with the computing system; compare the at least one output with at least one pre-defined output; generate a confidence level for the at least one current state attribute; and determine a current state attribute for the computing system, wherein the current state attribute defines a current state of the computing system.