Log Pattern Discovery via Hierarchy Tree Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional log analysis systems require manual setup of parameters, are slow, and lack flexibility, making it difficult to efficiently parse and analyze the vast number of logs generated by diverse systems like social networks and IoT devices, which often have unique formats and high volumes.
Innovation Solution
A system that automatically parses logs by generating patterns using unsupervised machine learning, building a pattern hierarchy tree, and selecting patterns based on similarity metrics, eliminating the need for user input parameters and optimizing memory and processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual parameter setting is used in conventional log analysis systems, then the system can be configured to analyze logs, but the process becomes troublesome and time-consuming for users who lack knowledge about optimal parameter values
Solution Approach 1:
The system automatically discovers log patterns and configures analysis parameters without requiring user input. The pattern discovery algorithm autonomously learns from log data, eliminating the need for manual parameter specification while maintaining effective log analysis capabilities
Solution Approach 2:
The system performs preliminary pattern discovery and parameter optimization automatically before the user needs to use the system. By pre-configuring patterns through automated learning from sample logs, the system eliminates the time users would otherwise spend on parameter setup and configuration
2Productivity
If conventional log analysis systems process large volumes of logs, then they can provide comprehensive analysis, but the processing speed becomes slow and lacks flexibility
Solution Approach 1:
The system segments log processing into distinct phases: pattern discovery, pattern matching, and anomaly detection. By dividing the processing pipeline into independent stages with optimized operations at each level, the system achieves both high throughput for large log volumes and fast processing speed for individual logs
Solution Approach 2:
The system dynamically adjusts processing parameters based on log characteristics and volume. The pattern discovery phase uses learned patterns to adaptively configure matching parameters, enabling the system to maintain high processing speed across varying log volumes and complexity levels
3Measurement precision
If users run multiple rounds of log analysis with different parameter values to achieve satisfaction, then they may find better patterns, but each round takes a long time due to slow conventional system performance
Solution Approach 1:
The system performs preliminary pattern discovery using automated algorithms to pre-configure optimal patterns before formal analysis begins. This preliminary learning phase eliminates the need for multiple manual analysis rounds, providing accurate patterns in a single execution and eliminating the time cost of iterative parameter tuning
Solution Approach 2:
The system incorporates feedback mechanisms where analysis results automatically inform pattern refinement. By continuously learning from analysis outcomes and adjusting patterns accordingly, the system achieves high pattern accuracy in fewer iterations, significantly reducing the time compared to conventional manual trial-and-error approaches
Data Source
AI summary
Systems and methods are disclosed for parsing logs from arbitrary or unknown systems or applications by capturing heterogeneous logs from the arbitrary or unknown systems or applications; generating one pattern for every unique log message; building a pattern hierarchy tree by grouping patterns based on similarity metrics, and for every group it generates one pattern by combing all constituting patterns of that group; and selecting a set of patterns from the pattern hierarchy tree.