Skipped Data Record Auto-Correction Through Error Pattern Matching
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Solution Overview
Problem
Existing data processing systems fail to automatically identify and correct unprocessed data records due to errors, leading to incomplete tasks, delayed processing, and uncertain resolution, often requiring manual intervention and causing significant delays.
Innovation Solution
An error resolution system with an auto-correction mechanism that monitors data processing systems, identifies error patterns from system logs, applies corrective actions, and reprocesses unprocessed records in real-time or near real-time, utilizing machine learning algorithms to match error patterns with predefined solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual intervention is used to identify and correct processing errors, then error resolution accuracy can be maintained, but processing time increases significantly and system productivity decreases
Solution Approach 1:
The system implements self-service through automated error detection, pattern recognition, and correction mechanisms. The error monitoring component continuously tracks processing errors, the pattern recognition component automatically identifies recurring error types, and the correction component applies predefined solutions without human intervention, enabling the system to resolve its own errors while maintaining high productivity
Solution Approach 2:
The system establishes feedback loops where processing errors are captured, analyzed, and used to generate corrective actions. The correction results are fed back into the processing system to prevent recurrence, creating a continuous improvement cycle that maintains accuracy while operating autonomously at high speed
2Productivity
If automated error correction is implemented, then processing speed and productivity improve, but system complexity increases due to additional monitoring and correction mechanisms
Solution Approach 1:
The error correction system is segmented into distinct functional components: error monitoring that captures processing errors, pattern recognition that identifies error types, and correction that applies solutions. This modular segmentation allows each component to be independently optimized and managed, reducing overall system complexity while maintaining high productivity
Solution Approach 2:
The system performs preliminary actions by pre-defining correction patterns and solutions for common error types. When errors occur, the system matches them against predefined patterns and applies corresponding corrections without requiring complex real-time decision-making, thereby improving productivity while keeping the correction mechanism relatively simple
3Reliability
If all processing errors are corrected immediately, then data completeness improves, but resource consumption increases due to reprocessing requirements
Solution Approach 1:
The system applies partial correction by focusing on the most frequent and impactful error patterns rather than attempting to correct every possible error type with equal effort. The pattern recognition component identifies and prioritizes common error patterns, applying corrections selectively to achieve substantial data completeness improvement while minimizing unnecessary reprocessing and resource consumption
Data Source
AI summary
Systems, methods, and other embodiments associated with error resolution and auto-correction are described. In one embodiment, a method includes, for a selected application that was executed by the computing system, accessing a system error log and identifying error messages that occurred during execution. Skipped data records that were not processed are identified. A resolving function is executed to resolve a selected error type that is associated with a group of skipped data records by: identifying an error pattern from an error message and matching it to a database of observed error patterns. In response to a match, retrieving a corrective action that is assigned to the observed error pattern and executing the corrective action to resolve the error on the group of skipped data records. The skipped data records are re-submitted and reprocessed where previous error should be resolved.