Log-file Analysis Engine Using Vector Grouping
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Solution Overview
Problem
Complex test and measurement systems generate dynamically changing log-files with varied formats and contents, making existing predefined root-based approaches ineffective for analysis.
Innovation Solution
A data-driven method that uses an analysis engine to identify and group similar log-files based on their content and patterns, without requiring human-defined rules, employing machine learning and natural language processing to provide compressed representations and labels for further analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If predefined root-based approaches are used for log-file analysis, then analysis can be performed on well-prepared log-files with specific formats, but the approach becomes inapplicable when log-file formats and contents dynamically change
Solution Approach 1:
The patent transforms log-files from their original text-based format into vector representations through compression and dimensionality reduction techniques. This parameter transformation allows the system to handle dynamically changing log formats by converting them into a standardized numerical space where similarity can be measured, thus resolving the contradiction between format adaptability and analysis reliability
Solution Approach 2:
The patent replaces traditional mechanical rule-based parsing approaches with data-driven machine learning methods. Instead of using predefined rules that require manual preparation, the system uses unsupervised learning algorithms to automatically identify patterns and group similar log-files, achieving both adaptability to new formats and reliability through statistical learning
2Quantity of substance
If log-files are made comprehensive to encompass all measurement information, then complete data is available for analysis, but the files become too large for human comprehension
Solution Approach 1:
The patent extracts essential features from comprehensive log-files by transforming them into compressed vector representations. This extraction process identifies and retains only the most relevant characteristics needed for similarity assessment, enabling the system to work with complete data while presenting manageable, condensed information for analysis
Solution Approach 2:
The patent creates simplified copies of the original log-files in the form of vector representations and similarity groups. These copies preserve the essential information needed for analysis while being much more manageable in size and structure, allowing humans to work with the copied representations rather than the full comprehensive logs
3Measurement precision
If manual rule preparation is performed for log-file analysis, then analysis accuracy can be maintained for known formats, but the process requires significant human interaction and time
Solution Approach 1:
The patent performs preliminary transformation of log-files into vector representations and pre-computes similarity relationships by grouping logs with similar characteristics. This preliminary action creates a structured foundation that enables fast, accurate analysis without requiring manual rule preparation for each new log format, thus reducing time loss while maintaining precision
Solution Approach 2:
The patent implements self-service through automated machine learning algorithms that automatically learn patterns from log-data and generate analysis rules without human intervention. The system serves itself by continuously improving its analysis capabilities through unsupervised learning, eliminating the need for manual rule preparation while maintaining high precision
Data Source
AI summary
A method of assessing a log-file is described. A log-file to be assessed is received. A data storage is accessed that includes several log-files. A group of log-files from the several log-files stored in the data storage is identified. The group of log-files includes log-files that are nearest to the log-file to be assessed. The identified group of log-files is returned to a user for further analysis. Further, a method of grouping several log-files as well as a system for processing at least one log-file are described.