Context Labeling for Tokenized Log Message Visualization
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Solution Overview
Problem
Existing templating algorithms for log messages lack contextual information, making it difficult to visualize or process log parameters and variables effectively, which is crucial for appropriate charting and data processing, and hinders machine learning systems from forecasting outages.
Innovation Solution
A device generates a template for log parameters, uses a language model to determine relevant context, and creates visualizations based on this context for user interfaces, incorporating contextual information to enhance understanding and facilitate deeper analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If templating algorithms are used to parse log messages, then log compression and visualization are enabled, but contextual information about parameters and variables is lost
Solution Approach 1:
The patent introduces an intermediary component (context enrichment module) that sits between the templating algorithm and the visualization system. This module extracts parameter names, variable names, and units from the template structure and attaches them as metadata to the parsed log data, thereby recovering contextual information without requiring complex reprocessing of the entire log message
Solution Approach 2:
The patent segments the log processing pipeline into distinct stages: templating, context enrichment, and visualization. By separating the context extraction step from the templating algorithm, the system recovers contextual information without increasing the complexity of the templating process itself
2Ease of operation
If contextual information is added to log parameters, then visualization and data processing improve, but system complexity increases
Solution Approach 1:
The patent performs context enrichment as a preliminary action during the log parsing phase, before visualization or analysis occurs. By extracting and attaching parameter context (names, units, types) early in the processing pipeline, the system enables improved visualization without adding complexity to subsequent operations
Solution Approach 2:
The context enrichment module serves multiple functions simultaneously: it extracts parameter names for labeling, identifies variable names for tracking, determines units for scaling, and classifies data types for appropriate visualization choices. This multi-functionality improves ease of operation without proportionally increasing system complexity
3Measurement precision
If manual context annotation is performed for log parameters, then visualization accuracy improves, but time consumption increases
Solution Approach 1:
The patent implements self-service context extraction where the system automatically derives parameter context (names, units, types) from the template structure and log message patterns without requiring manual annotation. The context enrichment module parses the template to identify parameter placeholders and infers their meaning from surrounding context, achieving high accuracy automatically
Solution Approach 2:
The patent replaces the mechanical process of manual context annotation with an automated computational approach. The context enrichment module uses pattern matching and template parsing algorithms to automatically extract parameter information, substituting human manual work with automated processing that is both faster and consistently accurate
Data Source
AI summary
In one implementation, a device generates a template for a particular parameter in one or more log files. The device uses a language model to determine a context for the particular parameter based on the template that would be relevant to an administrator. The device generates a visualization of the particular parameter based on the context. The device provides the visualization to a user interface for review by the administrator.