Automated Log Categorizer for Performance Indicator Detection
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Solution Overview
Problem
Existing log message analysis systems face challenges in efficiently processing large and complex log data sets to detect performance indicators, requiring manual assessment and lacking automated categorization capabilities.
Innovation Solution
The system automatically constructs a log categorizer by processing log messages to identify variable components, define ranges, and assign labels based on value distributions, allowing for sorting, filtering, and data presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual assessment of log messages is used, then accuracy of performance indicator detection is improved, but productivity is worsened due to large volumes of log data
Solution Approach 1:
The system automatically performs categorization and performance indicator detection without requiring manual assessment. The log analysis system processes log messages autonomously by extracting variables, determining distributions, and generating categorizers automatically, enabling the system to serve itself rather than relying on human operators to manually review each log message.
Solution Approach 2:
The patent replaces the mechanical manual assessment process with an automated computational system. Instead of human operators manually analyzing log messages, the system uses automated variable extraction, distribution analysis, and categorizer generation to detect performance indicators, substituting human mechanical analysis with automated information processing.
2Productivity
If automated categorization is implemented, then productivity is improved by processing large log data sets, but device complexity is worsened due to multiple processing components
Solution Approach 1:
The system employs a universal log analysis platform that performs multiple functions within a single integrated architecture. The same core components (variable extraction, distribution determination, categorizer generation) handle different types of log messages and performance indicators, allowing one system to serve multiple purposes rather than requiring separate specialized systems for each function.
Solution Approach 2:
The patent segments the log analysis process into distinct modular components: variable extraction, distribution determination, categorizer generation, and log message categorization. Each component performs a specific function and can be independently configured, allowing the system to process large volumes of data through organized, manageable segments rather than a monolithic complex structure.
3Measurement precision
If detailed analysis of all log messages is performed, then measurement precision is improved, but loss of time is worsened due to processing delays
Solution Approach 1:
The system performs preliminary analysis by automatically extracting variables from log messages and determining their distributions in advance. This preliminary processing creates a structured foundation that enables faster subsequent categorization and performance indicator detection, reducing the time required for detailed analysis while maintaining accuracy through pre-computed information.
Solution Approach 2:
The patent applies partial action by focusing analysis on the most relevant variables and distributions that directly impact performance indicator detection. Rather than performing exhaustive analysis on every aspect of each log message, the system identifies and processes the critical elements necessary for accurate performance detection, achieving sufficient precision without unnecessary time consumption.
Data Source
AI summary
A set of field values corresponding to a set of underlying fields are extracted from individual log messages. A space of potential values for underlying field(s) is identified. The space of potential values is segmented into value subspaces. Each value subspace is automatically associated with a category name. A definition for the new categorical field is generated, which indicates how a categorical value of the new categorical field depends on value(s) of the underlying field(s). For each log message, a categorical value is determined for the new categorical field based on the definition and the one or more values of the one or more underlying fields extracted from the log message. A presentation is generated that represents, for each log message, the particular category name corresponding to the categorical value determined for the log message.