Automated Log Filtering via Pattern Analysis
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Solution Overview
Problem
The vast amount of logging data generated by software services, containing hundreds of log entries per second, makes it challenging for administrators and support staff to efficiently identify relevant information for understanding behavior, performance, and resource usage, as existing methods lack effective filtering and analysis tools.
Innovation Solution
An automatic analysis technique that uses machine learning and natural language processing to identify common portions in log entries, indexing them for quick lookups, and recommending filters for efficient searching, allowing users to construct complex queries and narrow down log data to relevant entries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If log data is generated with high frequency and verbosity to capture detailed service behavior, then the completeness and reliability of logging data is improved, but the complexity and difficulty of analyzing and identifying relevant information increases
Solution Approach 1:
The patent introduces an automated analysis system that acts as an intermediary between the voluminous log data and human analysts. This system automatically processes, filters, and prioritizes log entries, transforming the raw complex data into manageable insights. The intermediary handles the complexity of analyzing hundreds of log entries per second, while providing simplified, relevant information to users.
Solution Approach 2:
The patent replaces manual log analysis mechanisms with automated computational systems. Instead of humans manually examining log entries, machine learning algorithms and natural language processing automatically analyze the data, identify patterns, and generate insights. This substitution handles the complexity through automated processing rather than human cognitive effort.
2Ease of operation
If traditional manual methods are used to examine log files, then the simplicity of the analysis process is maintained, but the time required to identify relevant information and track performance increases
Solution Approach 1:
The system performs preliminary analysis of log data automatically, pre-processing and organizing information before users need to examine it. Log entries are pre-filtered, pre-prioritized, and pre-formatted based on their relevance and importance. This preliminary action reduces the time users need to spend on manual examination while maintaining ease of operation.
Solution Approach 2:
The system provides automated feedback by continuously monitoring log data and immediately notifying users of relevant events, patterns, or anomalies. This real-time feedback mechanism eliminates the time delay associated with manual log examination, as the system automatically processes and reports findings as they occur.
3Loss of information
If comprehensive logging is implemented to capture all service behaviors and errors, then the amount of information available for analysis is improved, but the difficulty of filtering and searching through log entries increases
Solution Approach 1:
The system changes the parameters of log data representation by automatically transforming raw log entries into structured, standardized formats with consistent metadata and categorization. This parameter transformation makes filtering and searching more efficient by organizing the comprehensive information in a uniform, query-friendly structure.
Solution Approach 2:
The patent segments the comprehensive log data into distinct categories, types, and priority levels automatically. Log entries are divided into meaningful groups based on their content, source, and significance. This segmentation maintains all available information while making it easier to filter and search by dividing the large dataset into manageable, organized segments.
Data Source
AI summary
A request to explore a set of log entries is received. The set of log entries are analyzed to identify common portions in contents of at least a portion of the set of log entries. Based on the analysis that identified the common portions, filters to explore the set of log entries are automatically recommended.