Log Analysis Vector Space Remedial Actions
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Solution Overview
Problem
Existing methods for analyzing log files from computer systems are reactive, often identifying issues only after they occur, leading to downtime and reduced productivity.
Innovation Solution
The implementation of machine learning techniques to analyze log files by grouping related messages into blocks, processing them to remove irrelevant information, and projecting them into a vector space to identify patterns and associate them with remedial actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional log file analysis methods are used, then system issues are identified after they occur, but this results in downtime and reduced productivity
Solution Approach 1:
The system performs preliminary actions by continuously analyzing log files and detecting patterns that indicate potential system issues before they cause actual failures. The machine learning model is trained on historical log data to recognize early warning signs, enabling proactive intervention that prevents downtime and maintains productivity.
2Reliability
If machine learning techniques are implemented to analyze log files in real-time, then proactive issue identification is enabled, but this increases system complexity
Solution Approach 1:
The patent introduces an intermediary machine learning model that acts as a mediator between raw log files and system administrators. This model automatically processes and interprets complex log patterns, transforming unstructured log data into actionable insights without requiring complex manual analysis systems.
Solution Approach 2:
The system changes parameters by transforming log data from its original textual format into numerical feature representations that can be processed by machine learning algorithms. This parameter transformation enables automated pattern recognition while managing system complexity through structured data processing.
3Measurement precision
If log messages are grouped into blocks and processed to remove irrelevant information, then analysis precision is improved, but this increases processing time
Solution Approach 1:
The patent applies segmentation by dividing continuous log streams into discrete message blocks that can be independently processed. This segmentation enables parallel processing of multiple log blocks simultaneously, improving analysis precision through focused pattern recognition while reducing overall processing time through concurrent execution.
Solution Approach 2:
The system extracts and removes irrelevant information from log messages by identifying and filtering out noise, redundant data, and non-critical entries. This extraction process concentrates computational resources on meaningful patterns, improving identification precision while actually reducing processing time by eliminating unnecessary data analysis.
Data Source
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AI summary
The disclosed embodiments provide for identification of a remedial action based on analysis of a system log file. In some example embodiments, messages from the system log file are used as input to generate vectors within a vector space. Portions of the log messages may generate vectors that cluster into a region in the vector space. The region of vector space is associated with one or more remedial actions. The disclosed embodiments are configured, in some example embodiments, to perform the one or more remedial actions when activity in the log file maps to the region of vector space associated with the one or more remedial actions. In some example embodiments, a remedial action can include submitting a problem report to a problem tracking database.