AI Log Processing for Automatic Multi-Line Timestamp Detection
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Solution Overview
Problem
Existing log file processing techniques require prior user input specifying timestamp patterns, making them inefficient and prone to human error, especially when dealing with complex apparatuses that generate multi-line log messages.
Innovation Solution
A data-driven processing method using an AI model, such as a large language model (LLM), to automatically detect timestamp formats and regex patterns in log files without prior user input, enabling extraction of timeseries data from single-line and multi-line messages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If template-based timestamp detection is used, then timestamp extraction can be performed, but prior user input specifying string patterns is required which reduces efficiency and increases complexity
Solution Approach 1:
The processing system automatically detects timestamp formats and regex patterns by analyzing the log file itself, without requiring external user input. The system services itself by autonomously identifying patterns such as timestamps, log messages, and their structures through internal analysis algorithms.
Solution Approach 2:
The system performs preliminary analysis of the log file to detect timestamp formats and patterns before actual timestamp extraction begins. This preliminary detection phase identifies string patterns, regular expressions, and temporal information structures that are then used for efficient extraction.
2Measurement precision
If manual pattern specification is used for timestamp detection, then timestamp extraction can be performed, but human error increases and reliability decreases
Solution Approach 1:
The system eliminates human involvement in pattern specification by automatically detecting and identifying timestamp formats, log message structures, and regex patterns through internal analysis, thereby removing the source of human error.
Solution Approach 2:
The system uses feedback from analyzing multiple log entries to refine and confirm timestamp pattern detection. By examining the structure and content of log messages, the system validates detected patterns against actual data, ensuring accurate and reliable timestamp extraction.
3Reliability
If log files from complex apparatuses are processed, then comprehensive monitoring is achieved, but log file size increases making analysis challenging
Solution Approach 1:
The system extracts only the essential temporal information (timestamps) and relevant log message content from large log files, separating useful data from voluminous raw log data. This extraction process converts large log files into compact timeseries datasets suitable for analysis.
Solution Approach 2:
The system performs preliminary detection and classification of log message structures before extraction, identifying which portions of large log files contain meaningful temporal and event data versus redundant information, enabling efficient processing of comprehensive monitoring data.
Data Source
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AI summary
To process a log file of at least one apparatus (14), a processing system (20) obtains the log file. The processing system (20) identifies one or several multi-line log messages in the log file. The processing system (20) detects timestamps in several log messages of the log file, comprising communicatively interfacing with a data-driven processing model (12) to obtain timestamp-related information. The processing system (20) extracts timeseries data based at least on the timestamp-related information and performs at least one operation based on the timeseries data.