Log Error Analysis via Vector Categorization and Gantt Visualization
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Solution Overview
Problem
Log analysis is cumbersome due to the complexity and volume of log files, making it time-consuming to identify errors and their causes, especially when multiple files need to be analyzed simultaneously.
Innovation Solution
A system and method for categorizing and displaying log errors using a Gantt chart, employing predictive models like N-gram and Sent2Vec, and performing odd pattern analysis to identify anomalous errors, which includes converting error messages into error vectors, determining categories using a K-nearest neighbor algorithm, and applying community detection algorithms to identify error communities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual log analysis is performed, then detailed error inspection is possible, but analysis time and labor requirements increase significantly
Solution Approach 1:
The system performs automated log analysis by converting error messages to vectors, categorizing them using K-nearest neighbor algorithms, and generating visualizations without requiring manual inspection. The analysis tool processes log files autonomously, transforming detailed manual error inspection into an automated pipeline that maintains accuracy while eliminating time-consuming manual review.
Solution Approach 2:
The patent replaces manual mechanical log review with computational processes including error message vectorization, machine learning-based categorization, and automated pattern recognition. This substitution transforms the manual analysis process into an automated system that processes logs at scale without human intervention.
2Adaptability or versatility
If multiple log files are analyzed simultaneously, then comprehensive system insight is obtained, but complexity and resource strain increase
Solution Approach 1:
The system processes multiple log files by converting each error message into a vector representation and categorizing them independently using K-nearest neighbor algorithms. This segmentation allows parallel processing of errors from multiple files, maintaining comprehensive system insight while reducing the complexity of simultaneous analysis through standardized processing units.
Solution Approach 2:
The analysis tool is designed to handle multiple log files through a universal processing pipeline that accepts any log file format. The same vectorization and categorization algorithms process errors from different sources uniformly, enabling comprehensive multi-file analysis without increasing operational complexity.
3Productivity
If automated error categorization is implemented, then analysis efficiency improves, but computational resources and processing complexity increase
Solution Approach 1:
The system transforms error messages into vector representations using N-gram models and Sent2Vec algorithms, changing the parameter space from text to numerical vectors. This transformation enables efficient automated categorization through K-nearest neighbor algorithms while controlling computational resource consumption through optimized vector processing and categorization thresholds.
Data Source
AI summary
This disclosure describes implementations for categorizing and displaying a Gantt chart of categorized errors, as well as performing an odd pattern analysis to identify anomalous errors including obtaining a log file that includes a number of log entries where each log entry includes an error message. Converting each error message into an error vector using a predictive model. Determining a category for each error vector using a K-nearest neighbor algorithm, and generating a Gantt chart representing error messages arranged hierarchically by category as a function of time for display at a user interface.