Message Log Clustering for Telecommunications System State Analysis
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Solution Overview
Problem
Telecommunications systems generate vast volumes of message logs that are complex and difficult to analyze, making it challenging to understand and characterize system behavior, particularly in failure conditions, due to the high variety and number of status messages.
Innovation Solution
The method involves classifying messages to detect patterns, clustering similar messages, and determining system behavior by analyzing the totality of messages in the log, using statistical processes to determine dis-similarity and create clusters, which helps in understanding system state and transitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If automated analysis of message logs is attempted, then system behavior understanding is improved, but the complexity of analysis increases due to the volume and variety of messages
Solution Approach 1:
The patent segments the complex message log analysis task into distinct phases: message retrieval, classification into clusters based on similarity, pattern detection within clusters, and system state determination. This segmentation transforms an overwhelming monolithic analysis problem into manageable sequential steps, reducing analysis complexity while maintaining comprehensive system behavior understanding.
Solution Approach 2:
The patent introduces an intermediary classification layer that groups similar messages into clusters before detailed analysis. This intermediary step acts as a buffer between the raw message log and the analysis engine, reducing the complexity burden on the analysis system by pre-organizing data into meaningful groups that share common characteristics.
2Measurement precision
If all messages in the log are analyzed in detail, then system state determination accuracy is improved, but the time required for analysis increases
Solution Approach 1:
The patent segments the message set into clusters of similar messages, allowing the analysis system to process one cluster at a time rather than examining every individual message in detail. This segmentation enables efficient sampling and pattern recognition within clusters, maintaining accuracy while reducing overall analysis time.
Solution Approach 2:
The patent merges similar messages into clusters based on their characteristics and patterns. By combining multiple similar messages into a single representative cluster, the system reduces the total number of items requiring detailed analysis while preserving the essential information needed for accurate system state determination.
3Loss of information
If message logs are classified and clustered to detect patterns, then system behavior characterization is improved, but the processing complexity increases
Solution Approach 1:
The patent segments the processing task into distinct stages: message retrieval, classification into clusters, pattern detection within clusters, and system state characterization. This segmentation breaks down the complex processing workflow into manageable, sequential operations that can be implemented and maintained more easily.
Solution Approach 2:
The patent develops a universal classification and clustering framework that can handle diverse message types and system configurations. This multi-functional approach allows the same processing methodology to be applied across different telecommunications systems and message formats, reducing the need for system-specific customizations and lowering overall processing complexity.
Data Source
AI summary
A method that enables the run-time behavior of a system to be analyzed is disclosed. By analyzing the totality of the messages in the log or logs system behavior can be better understood. The illustrative embodiment of the present invention detects patterns in message logs, clusters similar messages, and determines system behavior based on the clustering of messages.