Event Log Tree Graphs for Automatic Cause-and-Effect Correlation
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Solution Overview
Problem
Manually analyzing system logs to diagnose issues is cumbersome, error-prone, and time-consuming, especially in complex systems where administrators struggle to correlate thousands of unique log entries to identify relevant events.
Innovation Solution
Implementing an event logging system that structures log entries as tree-like graphs, allowing for automatic determination of cause-and-effect relationships between events, enabling efficient storage and visualization of event histories without pre-defined correlation rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If administrators manually analyze system logs to diagnose issues, then they can identify relevant log entries, but the process becomes time-consuming and error-prone when dealing with thousands of unique log entries
Solution Approach 1:
The patent segments the overwhelming number of log entries into meaningful groups by identifying cause-and-effect relationships between events. Instead of presenting administrators with thousands of individual log entries, the system clusters related events together, allowing administrators to analyze problems at a higher level of abstraction while maintaining diagnostic accuracy.
Solution Approach 2:
The patent introduces an automated event correlation system as an intermediary between the raw log data and the administrator. This intermediary automatically analyzes log entries, identifies causal relationships between events, and presents synthesized problem scenarios to administrators, eliminating the time-consuming manual correlation process while preserving diagnostic precision.
2Reliability
If administrators manually parse logs to understand cause and effect relationships, then they can diagnose system problems, but the process requires extensive expertise and is prone to errors
Solution Approach 1:
The patent enables the system to serve itself by automatically analyzing log entries and identifying cause-and-effect relationships without requiring administrator expertise in log parsing. The automated correlation engine performs the complex analysis that previously required skilled administrators, making the process more reliable and accessible to users with varying levels of expertise.
Solution Approach 2:
The patent replaces the manual mechanical process of log parsing with an automated computational system. Instead of administrators manually examining and correlating log entries, the system uses automated algorithms to identify causal relationships between events, eliminating human error and expertise requirements while maintaining or improving diagnosis reliability.
3Loss of information
If system logs store all unique log entries for complex systems, then complete event histories are preserved, but storage resources are consumed by less relevant events
Solution Approach 1:
The patent extracts and retains only the essential cause-and-effect relationships from the complete log data. By identifying and storing only the meaningful causal links between events rather than all raw log entries, the system preserves the necessary event history for diagnostics while removing redundant information that consumes storage resources.
Solution Approach 2:
The patent discards less relevant log entries that do not contribute to cause-and-effect relationships while recovering and preserving the essential causal information. This selective retention strategy maintains event history completeness for diagnostic purposes while freeing storage resources by eliminating redundant or irrelevant data.
Data Source
AI summary
Systems and methods are described herein for logging system events within an electronic machine using an event log structured as a collection of tree-like cause and effect graphs. An event to be logged may be received. A new event node may be created within the event log for the received event. One or more existing event nodes within the event log may be identified as having possibly caused the received event. One or more causal links may be created within the event log between the new event node and the one or more identified existing event nodes. The new event node may be stored as an unattached root node in response to not identifying an existing event node that may have caused the received event.