Centralized Log Analysis with ML Pattern Detection
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Solution Overview
Problem
Existing log analysis systems require manual effort and significant computing resources to search through vast amounts of log data from applications, services, and server devices, making it difficult to identify relevant information for error detection, issue management, and trend analysis.
Innovation Solution
A centralized logging system that consolidates log data from various sources, performs natural language processing to convert log data into event data, and uses machine learning models to identify patterns, generate alerts, predict events, classify data, and provide recommendations, thereby automating the analysis process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of log data is performed, then accurate detection of issues is possible, but significant time and computing resources are required
Solution Approach 1:
The patent replaces manual mechanical analysis of log data with automated machine learning models. The system uses trained ML models to process log data, identify patterns, and detect issues automatically, eliminating the need for human analysts to manually examine vast amounts of log data while maintaining or improving detection accuracy.
Solution Approach 2:
The patent introduces machine learning models as an intermediary between raw log data and issue detection. These models serve as a bridge that automatically processes log data, extracts relevant features, identifies patterns, and generates alerts, thereby reducing both the time and manual effort required for analysis while preserving detection accuracy.
2Reliability
If manual analysis of log data is performed, then detailed issue management is possible, but significant computing resources are consumed
Solution Approach 1:
The patent extracts only the most relevant features and patterns from log data using machine learning models, rather than processing the entire dataset manually. The system identifies and focuses on critical indicators of issues, eliminating unnecessary computational overhead while maintaining reliable issue management through targeted analysis of key data points.
Solution Approach 2:
The patent transforms raw log data into structured event data with extracted features and patterns through machine learning processing. This parameter transformation allows the system to work with condensed, meaningful representations of log data rather than raw voluminous data, reducing computing resource consumption while preserving the quality of issue management through enhanced data representation.
3Productivity
If automated machine learning analysis is implemented, then analysis time is reduced, but system complexity increases
Solution Approach 1:
The patent divides the log analysis system into distinct modular components: log data ingestion, natural language processing for event extraction, multiple specialized machine learning models for different analysis tasks (pattern recognition, correlation analysis, classification), and alert generation. This segmentation allows each component to be developed, maintained, and optimized independently, managing overall system complexity while enabling high-throughput automated analysis.
Solution Approach 2:
The patent employs machine learning models that perform multiple functions: pattern identification, correlation matrix generation, event prediction, and classification. These multi-functional models reduce the need for separate specialized systems for each analysis task, thereby managing system complexity while maintaining high productivity through versatile automated analysis capabilities.
Data Source
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AI summary
A device may receive log data from application logs associated with applications, service logs associated with services, and server logs associated with server devices. The device may store the log data. The device may perform natural language processing on the log data to convert the log data into event data identifying events associated with categories. The device may process the event data, with a first machine learning model, to identify patterns in the event data and to generate an alert based on the patterns. The device may process the event data, with a second machine learning model, to generate a correlation matrix for the event data and to predict an event based on the correlation matrix. The device may process the event data, with a third machine learning model, to classify the event data based on the categories and to generate a recommendation based on classifying the event data.