Event Log Analyzer for Critical Event Prediction
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Solution Overview
Problem
Analyzing large volumes of diverse log data to identify critical events is challenging due to log volume, format diversity, and the presence of false records, making it difficult to predict and respond to critical events in a timely manner.
Innovation Solution
A system and method for automatically correlating log data to critical events by identifying candidate subsets of log messages within a predefined time window, determining their likelihood of predicting a critical event, and defining rules to perform actions when these events occur, allowing for proactive measures to be taken.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If log data is manually analyzed to identify critical events, then analysis accuracy can be maintained, but the time required and resources needed increase significantly
Solution Approach 1:
The system enables automated self-analysis of log data through machine learning models that automatically identify patterns, correlations, and critical events without requiring manual human intervention, thereby maintaining high accuracy while dramatically reducing the time and resources needed for analysis
Solution Approach 2:
Manual mechanical analysis processes are replaced with automated computational systems including event log analyzers, pattern recognition algorithms, and machine learning models that process log data electronically, substituting human effort with automated technological systems capable of rapid high-accuracy analysis
2Reliability
If all log messages are analyzed in detail, then comprehensive event detection is achieved, but the computational complexity and processing time increase
Solution Approach 1:
The log data analysis process is segmented into distinct stages including log collection, parsing, normalization, pattern matching, and event correlation. This segmentation allows each component to handle specific tasks efficiently, reducing overall computational complexity while maintaining comprehensive event detection through systematic processing of divided data streams
Solution Approach 2:
Different analysis methods and levels of detail are applied to different portions of log data based on their relevance and importance. Critical log messages receive more detailed analysis while routine messages undergo lighter processing, optimizing computational resources while ensuring comprehensive detection of important events through differentiated quality of analysis
3Productivity
If a predefined time window is used to identify candidate log messages, then the search scope is limited for faster processing, but relevant messages outside this window may be missed
Solution Approach 1:
The time window parameters are made dynamic rather than static, allowing the system to automatically adjust the search time window based on the specific event type, historical patterns, and contextual relevance. This dynamic adaptation enables faster processing for common events with predictable time windows while expanding the search scope when necessary to capture relevant messages, thus balancing speed and completeness
Solution Approach 2:
The system incorporates feedback mechanisms that continuously learn from analyzed events and adjust the time window parameters accordingly. By feeding back information about which time windows successfully captured relevant events, the system optimizes future search scopes, improving processing speed while reducing missed messages through data-driven parameter adjustment
Data Source
AI summary
The present disclosure involves systems, software, and computer implemented methods for correlating critical events to identified log data. An example event log analyzer can identify a set of log messages. One or more occurrences of a first critical event and a time of each of the occurrences are identified. One or more candidate subsets of log messages are identified. Each log message in each candidate subset is associated with a timestamp that is within a predefined time window prior to the time of an occurrence of the first critical event. A candidate subset of log messages is selected as a correlator of the first critical event. A rule is defined using the selected candidate subset of log messages. The rule defines a second critical event that correlates to the first critical event. The rule is associated with one or more actions to perform when the second critical event occurs.