Log Analysis System Using Hierarchical Plugin Execution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Analyzing large volumes of monitoring data from computing systems is challenging due to the spread of skills required to review different logs and the complexity of tracing errors across multiple log entries.
Innovation Solution
A log analysis system that generates hierarchical data structures and timeline data structures by collaboratively executing plugins, allowing for the recognition of information patterns in logs and the association of metadata and analysis data with log nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If large volumes of monitoring data are collected from multiple components, then the completeness of system monitoring is improved, but the difficulty of analyzing the data increases
Solution Approach 1:
The patent segments monitoring data into discrete log entries with structured fields, organizing the large volume of data into manageable units that can be individually processed and analyzed. Each log entry contains specific attributes (timestamp, component ID, event type, etc.) that enable systematic analysis rather than overwhelming raw data review.
Solution Approach 2:
The patent introduces an intermediary analysis system that includes plugins and processing engines to automatically interpret log data. These intermediaries translate raw monitoring data into meaningful insights, reducing the direct analytical burden on human operators while maintaining completeness of the monitored system.
2Measurement precision
If logs from multiple components are reviewed to trace errors, then the accuracy of root cause analysis is improved, but the time required for analysis increases
Solution Approach 1:
The patent implements preliminary structuring of log data with standardized fields and hierarchical organization before analysis is needed. Log entries pre-contain relevant attributes (component identifiers, timestamps, event types) that are organized in advance, enabling rapid retrieval and correlation during incident analysis without requiring time-consuming manual parsing.
Solution Approach 2:
The patent creates a universal log analysis framework that handles multiple component types and error scenarios through a single integrated system. The analysis engine and plugins provide multi-functional capabilities to correlate logs across different components, trace errors through the system, and perform root cause analysis uniformly regardless of the specific component or error type involved.
3Measurement precision
If specialized skills are distributed across different persons to review different logs, then the expertise required for accurate analysis is improved, but the coordination complexity increases
Solution Approach 1:
The patent implements self-service capabilities through automated analysis plugins and processing engines that independently interpret and correlate log data without requiring constant human coordination. The system autonomously performs data correlation, error tracing, and pattern recognition, reducing the need for coordinated review sessions while preserving specialized analytical capabilities through the automated framework.
4Adaptability or versatility
If log data is presented in different data formats from different components, then the adaptability of the monitoring system is improved, but the difficulty of processing uniform data increases
Solution Approach 1:
The patent applies parameter changes by transforming diverse log formats into a unified structured schema with standardized fields (timestamp, component ID, event type, etc.). The system dynamically adjusts and normalizes data parameters from different sources into consistent formats, enabling uniform processing while maintaining adaptability to various original log structures through the transformation layer.
Data Source
AI summary
Plugins that are independently written are executed in a collaborative manner to analysis a log. A plugin executing with respect to a particular node of a hierarchical data structure determines values for a set of keys based on information of the particular node and/or any ancestor nodes, and information stored in a shared repository. The plugin stores the values for the keys as additional information of the particular hierarchical node and/or into the shared repository. The plugin does not access information of non-ancestor nodes when executing with respect to the particular hierarchical node. Each plugin writes into and retrieves from the shared repository using the shared naming convention, thereby sharing information. The sequence of execution of the plugins is not dependent on dependencies amongst the plugins. If a dependent plugin requiring an output from a requisite plugin is first executed, the dependent plugin is flagged as pending and subsequently re-executed.