Automated Log Data Analysis System with Predictive Anomaly Detection
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Solution Overview
Problem
Current methods for analyzing log data files are time-consuming and require significant computational resources, leading to inefficiencies in identifying and resolving issues.
Innovation Solution
A method and system for automating the analysis of log data files, which involves preprocessing the files to generate a transformed format, detecting anomalies, performing predictive analysis, generating reports, receiving user feedback, and updating databases based on that feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of log data files is performed, then developers can identify errors and root causes, but the process is time-consuming and requires significant human expertise
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated AI-based system that uses machine learning models to detect anomalies and predict root causes in log data files, eliminating the need for human developers to manually walk through and analyze each log entry
Solution Approach 2:
The system performs self-service by automatically analyzing log data, detecting anomalies, and providing root cause predictions without requiring human intervention. The AI model continuously learns from feedback to improve its analysis accuracy autonomously
2Reliability
If developers manually analyze log data files, then they can determine root causes and fix errors, but productivity is reduced due to the lengthy analysis process
Solution Approach 1:
The system performs preliminary action by proactively detecting anomalies and predicting root causes before they manifest as critical failures. The AI model continuously monitors log data and identifies potential issues early, allowing for preventive maintenance and faster resolution when problems occur
Solution Approach 2:
The patent implements feedback mechanisms where the system receives feedback from users about its analysis accuracy and continuously improves its models. This feedback loop ensures that the AI system becomes more reliable over time while maintaining high productivity by reducing manual intervention needs
3Productivity
If automated analysis systems are implemented, then analysis time is reduced, but the system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex analysis system into modular components: data collection modules, anomaly detection modules, root cause prediction modules, and feedback processing modules. Each module handles a specific function independently, making the overall complex system more manageable and easier to implement
Solution Approach 2:
The system uses an intermediary AI model layer that simplifies the complexity between raw log data and actionable insights. The AI models act as intermediaries that process complex patterns and translate them into understandable predictions and recommendations, reducing the complexity burden on users
Data Source
AI summary
A method and system for automating analysis of log data files is disclosed. In some embodiments, the method includes pre-processing a log data file to generate a transformed log data file. The method further includes analyzing the transformed log data file to detect one or more of a plurality of anomalies in the transformed log data file; performing a predictive analysis for the one or more of the plurality of anomalies detected in the transformed log data file. The method further includes generating a report based on the predictive analysis performed for each of the one or more of the plurality of anomalies; receiving a feedback from an end-user based on the report generated for each of the one or more of the plurality of anomalies; and updating a database based on the feedback from the end-user for each of the one or more of the plurality of anomalies.