Deep Learning Log Analysis for Automated Error Prediction
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Solution Overview
Problem
Current methods for analyzing computer log files are inefficient and costly, as they require extensive expert knowledge and cannot effectively identify and predict errors in large volumes of data, especially in systems with frequent updates, leading to delayed issue resolution.
Innovation Solution
The use of deep learning methods and intelligent operations algorithms to parse log data by session, represent it in a matrix, and apply statistical pooling and Association Rules Analysis, along with Recursive Neural Networks, to identify root causes and predict errors, enabling automatic solution implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual log analysis is performed by experts, then error identification accuracy is improved, but analysis time and cost increase significantly
Solution Approach 1:
The patent segments log analysis into multiple specialized components: error pattern recognition, root cause analysis, and solution recommendation. Each component is handled by dedicated algorithms that process specific aspects of log data independently, improving both accuracy and efficiency by dividing the complex analysis task into manageable segments.
Solution Approach 2:
The patent replaces manual expert analysis (mechanical human cognition) with automated machine learning models and natural language processing algorithms. These systems automatically parse, interpret, and analyze log files, eliminating the time-consuming manual process while maintaining or improving accuracy through consistent application of analytical rules.
2Productivity
If automated log analysis tools are used, then analysis speed is improved, but ability to identify complex errors and root causes deteriorates
Solution Approach 1:
The patent introduces natural language processing as an intermediary layer between automated log parsing and root cause identification. The NLP component translates raw log entries into meaningful contexts, enabling automated systems to understand nuanced error patterns and relationships that simpler automated tools would miss, thereby maintaining high accuracy while preserving fast automated processing.
3Reliability
If extensive log data is analyzed to improve error detection, then detection capability is improved, but computational resources and processing time increase
Solution Approach 1:
The patent extracts and focuses only on the most critical and informative log entries for analysis, rather than processing entire log files. By identifying and extracting key error patterns, warning signs, and relevant contextual information, the system achieves high error detection capability while minimizing computational resource consumption by ignoring redundant data.
4Reliability
If manual analysis of massive log volumes is performed, then comprehensive error coverage is improved, but cost and time requirements increase exponentially
Solution Approach 1:
The patent implements self-service automated analysis systems that independently process and analyze log data without requiring human expert intervention for each analysis task. The system automatically detects errors, identifies root causes, and generates solution recommendations, providing comprehensive error coverage at scale while eliminating the need for extensive expert resources.
Data Source
AI summary
A computer-implemented method of training, using a computer log file, an application error prediction engine to identify one or more application errors includes parsing the computer log file into a plurality of data sets. Each data set is associated with a unique computing session having a session identifier and ending in an application or. The method also includes extracting, from each data set, values for a specified set of parameters in each data set. The method also includes encoding the extracted values for each data set into a corresponding data structure. The method also includes generating, for each data structure, a corresponding vector, the corresponding vectors collectively forming a matrix. The method also includes calculating, based on the matrix, a set of clusters, each cluster corresponding to a known error type, the set of clusters used to create a model used to identify new error types.