Automated Error Classification in Batch Failure Logs
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Solution Overview
Problem
Enterprise computing systems face challenges in managing and resolving batch execution failures, as they generate a large number of errors within a short period, overwhelming system administrators and requiring manual intervention to classify and remediate errors.
Innovation Solution
An automated failure classification and remediation process that compares character strings in the failure log to existing categories, determines remediation actions, and creates new categories when necessary, reducing administrative workload and improving batch process execution efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification and remediation of errors is performed, then accuracy in error handling can be maintained, but the time and resources required increase significantly due to the large volume of errors
Solution Approach 1:
The system performs self-service by automatically classifying errors using machine learning models and executing remediation actions without human intervention. The automated error classification system analyzes error logs, identifies patterns, and applies fixes autonomously, eliminating the need for manual error handling while maintaining high accuracy through trained algorithms.
Solution Approach 2:
The patent replaces the mechanical manual process of error classification and remediation with an automated computational system. Machine learning models and algorithms substitute human administrators in analyzing error patterns, categorizing failures, and applying fixes, thereby dramatically reducing the time required while preserving classification accuracy through intelligent automation.
2Productivity
If automated error classification is implemented, then productivity increases by reducing administrative workload, but system complexity increases due to the need for machine learning models and processing infrastructure
Solution Approach 1:
The system segments the error classification task into distinct components: error log ingestion, pattern recognition through machine learning models, error categorization, and remediation execution. This modular architecture allows each component to be independently optimized and managed, reducing overall system complexity while maintaining high productivity through specialized processing at each stage.
Solution Approach 2:
The automated error classification system is designed as a universal platform that can handle multiple types of errors across different batch processes. The machine learning models are trained to recognize diverse error patterns and the system can apply remediation actions across various system components, providing multi-functional capability that justifies the initial complexity investment through broad applicability and sustained productivity gains.
3Reliability
If all errors are processed individually, then each error can be addressed with specific remediation, but the overall resolution time increases due to the large number of errors
Solution Approach 1:
The system merges multiple individual error processing operations into a unified automated workflow. By combining error detection, classification, and remediation into a single integrated process that operates on all errors simultaneously, the system maintains complete error resolution coverage while achieving high processing speeds through parallel execution and automated orchestration of remediation actions across multiple errors.
Data Source
AI summary
Techniques are disclosed relating to a method that includes accessing, by a failure management program, a failure log that includes a plurality of character strings corresponding to errors that are associated with execution of one or more batch processes. The failure management program may compare a particular character string of the plurality of character strings to a set of character strings that are associated with respective ones of a plurality of failure categories. This comparing may include determining whether particular keywords that are included in respective ones of the set of character strings are included in the particular character string. In response to the comparing, the failure management program may assign a particular error corresponding to the particular character string to a particular failure category, or may determine a new failure category if the particular character string does not match an existing failure category.