Visual Error Signatures for Enterprise System Diagnostics
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Solution Overview
Problem
Information processing systems face challenges in monitoring, diagnosing, and remediating errors due to increasing complexity and errors caused by misconfiguration, faults, and security threats, which can expose enterprises and users to risks.
Innovation Solution
The method involves generating visual error signatures through graph-based visualization of enterprise system operations, using machine learning models to classify errors, and identifying remediation actions from historical data to address detected errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the number of assets in an enterprise system grows, then the system's functionality and capacity increase, but the number of errors encountered increases
Solution Approach 1:
The system automatically monitors, diagnoses, and remediates errors using machine learning models and automated workflows, enabling the enterprise system to self-correct errors without manual intervention, thus maintaining reliability as the number of assets grows
Solution Approach 2:
The system implements continuous feedback loops where error data from assets is collected, analyzed by machine learning models, and used to automatically trigger remediation actions, creating a closed-loop system that adapts and improves over time to maintain system reliability
2Productivity
If traditional error monitoring and diagnosis methods are used, then the process is simple to implement, but the efficiency and accuracy of error diagnosis decrease
Solution Approach 1:
The patent replaces traditional manual or rule-based error monitoring and diagnosis methods with machine learning models that automatically analyze error data, generate visual signatures, and classify errors, significantly improving diagnosis efficiency and accuracy while managing complexity through automated intelligence
3Loss of time
If manual error diagnosis and remediation processes are used, then the system complexity is low, but the time required for error resolution increases
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models on historical error data, pre-generating visual error signatures, and pre-establishing remediation workflows, enabling rapid automatic response when errors occur without requiring manual analysis or decision-making
Solution Approach 2:
The system automatically executes the full error management lifecycle including detection, visual signature generation, machine learning-based classification, and remediation action execution, eliminating manual intervention and dramatically reducing error resolution time
Data Source
AI summary
A method includes detecting an error that has occurred in one or more assets of an enterprise system and generating a visual error signature of the detected error, the visual error signature comprising at least a portion of a graph-based visualization of operation of the assets. The method also includes providing the generated visual error signature for the detected error as input to a machine learning model and utilizing the machine learning model to classify the visual error signature for the detected error as belonging to at least a given one of a plurality of error classes, the machine learning model being trained using historical visual error signatures for previously-detected errors. The method further includes identifying at least one action taken to remediate each of one or more previously-detected errors of the given error class and remediating the detected error utilizing one or more of the identified actions.