Automated Log Pattern Recognition for Data Mining Efficiency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Organizations face challenges in understanding and deriving business value from log files due to their complexity and the need for manual interaction, which requires specialized technical skills and repetitive data classification and mining efforts.
Innovation Solution
A method and system that automatically examine log files to determine their structure and patterns, recognize fields, and provide enhancement options by using a repository to store and retrieve known patterns, allowing for automatic data export to a database and suggesting field names and mining options based on content types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual interaction with log files is used to understand and process data, then data analysts can derive insights from log files, but it requires specialized technical skills and repetitive data classification and mining efforts
Solution Approach 1:
The system performs preliminary actions by automatically classifying log data and identifying patterns before the data analyst needs to analyze it. The log data classifier examines log files and pre-categorizes data into relevant classes, so when the analyst views the data, the repetitive classification work has already been completed, reducing both time loss and skill requirements
Solution Approach 2:
The system enables self-service by allowing log files to be automatically processed and classified without requiring specialized manual intervention. The automated classification system serves itself by identifying patterns and categorizing data based on predefined rules and machine learning, eliminating the need for analysts to manually classify each log entry
2Quantity of substance
If log files are partitioned based on maximum file size, then storage management is improved, but the log files become difficult to understand and analyze
Solution Approach 1:
The system introduces an intermediary layer between the partitioned log files and the analyst. The log data classifier acts as a mediator that automatically examines partitioned log files, identifies patterns across partitions, and presents unified insights without requiring the analyst to manually piece together information from multiple partitioned files
Solution Approach 2:
The system applies segmentation by dividing the analysis process into manageable components that work on partitioned log files independently. Each partition can be classified and analyzed separately by the automated system, then the results are aggregated to provide comprehensive insights, making large partitioned log files easier to understand
3Productivity
If automated pattern recognition is implemented in log files, then processing efficiency is improved, but the system complexity increases
Solution Approach 1:
The system manages complexity by changing parameters in a controlled manner. It starts with predefined classification rules and gradually incorporates more complex patterns only when needed. The log data classifier adjusts its complexity parameters based on the specific log file being analyzed, using simple rule-based classification for straightforward logs and more sophisticated pattern recognition only when necessary
Solution Approach 2:
The system applies partial action by implementing automated pattern recognition only for the most critical and frequently occurring log patterns. Rather than attempting to automatically analyze every possible log pattern with equal complexity, the system focuses on the most valuable patterns first, providing significant productivity improvements while keeping system complexity manageable
Data Source
AI summary
According to some embodiments, a method and an apparatus of analyzing log files comprises sampling a log and determining a structure associated with the log file based on the sampling and a pattern within the structure. If the structure and the pattern are stored in a repository, data from the log file will be exported into a database based on the determined pattern.