Log Message Pattern Analysis for Industrial Event Classification
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Solution Overview
Problem
Manual review of large volumes of log files in industrial plants is infeasible due to the complexity and heterogeneity of systems, requiring extensive domain expertise and is not scalable, and existing automated methods fail to capture hierarchical dependencies and domain-specific semantics.
Innovation Solution
A method involving vector space representation, cosine similarity, and clustering is applied to semi-structured log messages to identify templates and anomalies, enabling automatic interpretation and classification of log events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of log files is performed by domain experts, then interpretation accuracy is improved, but productivity deteriorates due to time-consuming analysis and infeasibility with large volumes
Solution Approach 1:
The patent introduces an automated log analysis system that acts as an intermediary between raw log files and domain experts. The system performs initial filtering, clustering, and anomaly detection, presenting only relevant findings to experts for verification. This mediator approach maintains high interpretation accuracy through expert oversight while dramatically improving productivity by automating the bulk analysis work.
Solution Approach 2:
The system enables self-service log analysis by automatically performing classification, clustering, and anomaly detection without requiring domain expert intervention for every log entry. The automated system serves itself by processing large volumes of logs, identifying patterns, and generating insights that would otherwise require extensive manual analysis, thereby resolving the contradiction between accuracy and productivity.
2Measurement precision
If extensive domain expertise training is provided to interpret log files, then measurement precision is improved, but loss of time increases due to training requirements
Solution Approach 1:
The automated analysis system serves as an intermediary that encapsulates domain knowledge in algorithms and models, eliminating the need for individual experts to undergo extensive training. The system pre-processes logs, identifies patterns, and presents interpreted results, allowing users to achieve high interpretation accuracy without investing significant time in domain expertise training.
3Adaptability or versatility
If knowledge bases with interpretation rules are built manually, then adaptability is improved for specific systems, but device complexity increases and building time is lost
Solution Approach 1:
The system performs preliminary action by automatically generating and updating knowledge bases through unsupervised learning from log data. Instead of manually building complex knowledge bases with system-specific interpretation rules, the system pre-processes logs, identifies patterns, and automatically constructs adaptive knowledge structures that evolve with the data, reducing both complexity and building time while maintaining adaptability.
Data Source
AI summary
In order to be able to confidently interpret log files of various system domains, experts need to undergo extensive training. Knowledge about one system does not necessarily transfer to other systems. Domain experts manually inspect log files, trying to find anomalies and concerning events. In some cases, domain experts may be assisted by log monitoring tools that match hand-crafted regular expressions against log entries or are use regular expressions to cluster log entries into groups. The proposed method advantageously identifies sequence patterns from semi-structured text log messages and classifying the identified patterns (e.g., blocks or a portion of a block) into categories (e.g., error, warning, and info). The identified patterns represent higher-level events that happen in the industrial plant (e.g., a plant-wide update process) and important sections may be automatically highlighted in a log file to human reviewers/domain experts.