Log Data Stream Abnormality Detection Using Statistical Graph Analysis
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Solution Overview
Problem
Current methods for analyzing log data in computational systems are impractical for real-time identification of abnormalities due to the volume, complexity, and velocity of data, particularly in IT and cloud computing environments, where retrospective analysis is cumbersome and inefficient.
Innovation Solution
The system converts log data streams into meta-data represented as a graph, allowing for real-time analysis by identifying dominant statistical patterns and comparing current data with historical patterns to detect abnormalities, thereby providing near real-time detection of abnormal event sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If retrospective analysis of log files is performed, then abnormalities can be identified, but real-time detection capability is lost
Solution Approach 1:
The system pre-processes log data streams during normal operation to build statistical models and baseline behavior patterns before abnormalities occur. This preliminary analysis enables real-time detection by comparing current events against pre-established norms, eliminating the time delay inherent in retrospective analysis while maintaining detection accuracy
2Reliability
If complete log data is stored for analysis, then comprehensive abnormality detection is possible, but data storage requirements increase
Solution Approach 1:
The system extracts only the essential statistical features and behavioral patterns from complete log data, storing compressed representations rather than raw data. By taking out only the critical information needed for anomaly detection (statistical models, baseline patterns, event frequency distributions), the system maintains comprehensive detection capability while dramatically reducing storage requirements
Solution Approach 2:
The system transforms raw log data into different parameter representations (statistical aggregates, frequency distributions, behavioral metrics) that capture the essence of system behavior in compact form. This parameter transformation enables comprehensive analysis using reduced data volumes by changing the representation from detailed raw events to condensed statistical features
3Measurement precision
If detailed statistical analysis is performed on log streams, then accurate abnormality detection is achieved, but processing complexity increases
Solution Approach 1:
The system segments the complex analysis task into distinct modular components: event parsing, statistical feature extraction, baseline model building, real-time comparison, and anomaly scoring. Each module handles a specific aspect of the analysis, reducing overall system complexity while maintaining detection accuracy through specialized processing at each stage
Data Source
AI summary
This disclosure presents systems and methods for run-time analysis of streams of log data for abnormalities using a statistical structure of meta-data associated with the log data. The systems and methods convert a log data stream into meta-data and perform statistical analysis in order to reveal a dominant statistical pattern within the meta-data. The meta-data is represented as a graph with nodes that represent each of the different event types, which are detected in the stream along with event sources associated with the events. The systems and methods use real-time analysis to compare a portion of a current log data stream collected in an operational window with historically collected meta-data represented by a graph in order to determine the degree of abnormality of the current log data stream collected in the operational window.