Error Message Deduplication and Sequence Analysis
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Solution Overview
Problem
Existing computing systems face inefficiencies in managing and resolving large volumes of error messages, leading to inefficient use of computing resources and difficulties in prioritizing and addressing high-severity issues within production host environments.
Innovation Solution
A method involving a message resolution manager that performs error message deduplication, generates message sequences, applies a message sequence frequency algorithm to identify high-severity sequences, and initiates targeted error message resolution, potentially utilizing machine learning for improved analysis and future error handling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If error message deduplication and sequence analysis are performed, then error message management efficiency is improved, but computing resource consumption increases
Solution Approach 1:
The patent segments the error message processing task into distinct phases: deduplication phase, sequence generation phase, and frequency analysis phase. Each phase processes a specific aspect of error messages independently, allowing for optimized resource allocation at each stage rather than consuming full resources throughout the entire process.
Solution Approach 2:
The system dynamically adjusts processing parameters such as sequence length thresholds, frequency cutoff values, and deduplication windows based on the volume and characteristics of incoming error messages. This allows the computing resources to be scaled appropriately to the actual workload, improving efficiency without consistently high resource consumption.
2Reliability
If all error messages are processed and resolved, then system reliability is improved, but time consumption increases
Solution Approach 1:
The patent applies different processing intensities to different error message sequences based on their severity and frequency characteristics. High-severity sequences identified through the frequency algorithm receive prioritized and more thorough analysis, while less critical sequences receive standard processing. This localized quality approach ensures critical issues are resolved quickly without spending equal time on all error messages.
Solution Approach 2:
The system implements feedback loops where resolved error sequences update the frequency algorithm's knowledge base, improving future prioritization accuracy. This allows the system to learn from past resolutions and progressively improve its ability to identify critical sequences, reducing overall processing time while maintaining or improving reliability.
3Measurement precision
If message sequence frequency algorithm is applied, then high severity issues are identified, but device complexity increases
Solution Approach 1:
The frequency algorithm performs preliminary analysis of error message patterns and establishes baseline severity thresholds before actual error resolution begins. This pre-computed knowledge structure allows the system to quickly classify new error sequences without performing complex real-time analysis, maintaining measurement precision while reducing operational complexity.
Data Source
AI summary
A method for managing error messages includes obtaining, by a message resolution manager, a plurality of error messages, performing an error message consecutive deduplication on the plurality of error messages to obtain a plurality of deduplicated error messages, generating a plurality of message sequences using the plurality deduplicated error messages, applying a message sequence frequency algorithm to the plurality of message sequences to obtain a high severity message sequence list, and initiating an error message resolution on at least one message sequence specified in the high severity message sequence list.