Context-Aware Knowledge Graph for Computing Error Root Cause Tracking
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Solution Overview
Problem
Conventional software development and testing methodologies fail to comprehensively track the root cause of computing errors, leading to resource wastage and inefficiencies in identifying and addressing errors at their source.
Innovation Solution
A context-aware knowledge graph model is generated to analyze dependencies between computing job operations, tracing and correlating errors to their root causes, using a flagging algorithm and machine learning to predict future errors and streamline alert management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional testing methodologies are used to track computing errors, then the process is simple and familiar, but the root cause identification is incomplete and resources are wasted
Solution Approach 1:
The error tracking system is segmented into multiple components: error collection module, knowledge graph generation module, root cause analysis module, and visualization module. This segmentation allows the system to handle complex error tracking tasks through specialized sub-components, improving root cause identification accuracy while managing system complexity through modular design
Solution Approach 2:
A knowledge graph is introduced as an intermediary data structure between error collection and root cause analysis. The knowledge graph stores and organizes error information, job dependencies, and contextual relationships, enabling more accurate root cause identification without directly increasing the complexity of the core analysis algorithms
2Loss of energy
If comprehensive error tracking is implemented to identify root causes, then resource waste is reduced, but the system complexity and implementation difficulty increase
Solution Approach 1:
The system performs preliminary actions by collecting and storing error information, job dependencies, and contextual data in a knowledge graph before root cause analysis is needed. This pre-processing enables faster and more accurate root cause identification when errors occur, reducing the computing resources required during actual error troubleshooting
Solution Approach 2:
The system implements feedback mechanisms where root cause analysis results are used to refine and update the knowledge graph. This continuous feedback loop improves the system's ability to identify root causes over time while optimizing resource allocation, as the system learns from past errors and becomes more efficient at preventing resource waste
3Productivity
If manual error tracking is used, then the system is easy to implement, but time consumption and efficiency are significantly reduced
Solution Approach 1:
The system implements self-service capabilities through automated error collection, knowledge graph generation, and root cause analysis. The system automatically tracks errors, analyzes job dependencies, and identifies root causes without requiring manual intervention, significantly improving error tracking efficiency and reducing the time required for error analysis
Solution Approach 2:
Manual mechanical error tracking processes are replaced with automated computational systems. The system uses algorithms to collect errors, generate knowledge graphs, and analyze root causes, substituting human manual analysis with automated computational processes that are faster and more efficient while reducing time consumption
Data Source
AI summary
A method and system for a generating a context-aware knowledge graph model that derives a root cause of computing system errors. The method includes receiving computing job operations comprising computing system errors; compiling a data dependency list for dependencies between the computing job operations; and compiling a data structure dictionary based on the data dependency list. The method further includes generating a data structure array based on the data structure dictionary with data keys and data values, and generating the context-aware knowledge graph model by parsing and iterating through the data structure array to create nodes in the context-aware knowledge graph model and link the nodes via edges based on a key value pairing between the data keys and the data values that derives the root cause of the computing system errors by tracing and correlating of the dependencies. The method further includes displaying the context-aware knowledge graph model.