Coherent Log File Representation for Reliable Industrial Analysis

The system performs self-service by automatically analyzing log files using machine learning models without requiring expert intervention. The trained model autonomously processes diverse log formats, extracts patterns, and generates insights, eliminating the need for manual expert analysis while maintaining high reliability through the learned patterns from training data.

The patent replaces the mechanical system of manual expert analysis with an automated machine learning-based system. The mechanical process of experts manually examining log files is substituted by computational algorithms that automatically process, parse, and analyze log data, significantly reducing time consumption while maintaining or improving analysis reliability.

The system performs preliminary action by pre-training machine learning models on diverse log file data before actual analysis. During the training phase, the model learns correct patterns and relationships from labeled examples, so that during deployment, it can automatically apply these learned patterns without the errors associated with manual regular expression definition. This preliminary training ensures both speed and accuracy.

The patent changes the fundamental parameters of log analysis from rule-based regular expressions to machine learning models. This parameter change transforms the analysis approach from static pattern matching to dynamic, adaptive pattern recognition that learns from data, improving both accuracy and reliability while maintaining automated processing speed.

The patent implements universality by designing a single machine learning model that can process multiple diverse log file formats. The model is trained on various log structures and formats, enabling it to universally handle different logging systems and sources without requiring separate processing pipelines for each format, thus maintaining versatility while controlling complexity.

The machine learning model acts as an intermediary layer between diverse log file formats and the analysis process. Instead of creating complex conversion pipelines for each format pair, the model serves as a universal mediator that can ingest various formats and output standardized insights, simplifying the overall system architecture while maintaining broad compatibility.