NLP Classification Module for Error Message Cataloging
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Solution Overview
Problem
Conventional monitoring systems fail to track and catalog issues, leading to repeated resolutions of the same issues, wastage of resources, and missed trend identification that could pinpoint issue hotspots.
Innovation Solution
A monitoring system incorporating a classification module with natural language processing and machine learning to classify and understand error messages from multiple platforms, recommending solutions based on historical data by aggregating, standardizing, and analyzing messages across different formats and sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional monitoring systems are used to determine issues, then issue detection is achieved, but issue tracking and cataloging are not performed leading to repeated resolutions
Solution Approach 1:
The system segments the monitoring functionality into distinct modules: issue detection module, issue tracking module, and recommendation module. Each module handles specific aspects of issue management independently, allowing comprehensive tracking without overwhelming the system.
Solution Approach 2:
The system implements feedback loops where resolved issues are cataloged and stored in a database. This feedback mechanism enables the system to learn from past issues and provide recommendations for similar future issues, preventing information loss and enabling continuous improvement.
2Device complexity
If issues are not tracked and cataloged, then system simplicity is maintained, but repeating issues are ignored and resources are wasted
Solution Approach 1:
The system performs preliminary actions by pre-processing and standardizing issue data as it is generated. Issues are normalized, categorized, and stored in a structured format in advance, so that when similar issues occur, the system can quickly retrieve and apply solutions without extensive processing delays.
Solution Approach 2:
The system creates standardized copies of issue data in a uniform format. By normalizing diverse issue descriptions into consistent templates, the system enables efficient storage, retrieval, and comparison of issues, improving productivity without proportionally increasing complexity.
3Ease of manufacture
If monitoring systems do not track issues, then implementation simplicity is preserved, but issue trends and hotspots cannot be identified
Solution Approach 1:
The issue tracking module serves multiple functions simultaneously: it tracks individual issues, categorizes them by type and severity, stores them in a standardized database, and enables trend analysis. This multi-functionality allows comprehensive information retention without requiring separate specialized systems for each function.
Data Source
AI summary
Systems and methods that determining a solution for a real-time message are provided. Multiple messages of different types are received from multiple platforms. The messages were generated in response to errors caused by applications monitored by the platforms. For each message, a language processing system determines the content of the message and the machine learning system determines a classification of the message. The set of message candidates are generated by comparing the classification and the content of the message to historical messages. From the set of message candidates, solution messages are identified. A recommended solution is determined from the solution messages.