Error Flow Diagrams for Application Transaction Root Cause Identification
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Solution Overview
Problem
Identifying the root cause of errors in application transactions across multiple layers of a computing system is challenging due to the complexity of error interactions and the masking of primary errors by secondary errors.
Innovation Solution
A system that collects and visualizes error data through an error flow diagram, allowing IT professionals to infer causation by linking errors in a graphical representation, enabling easier identification of the root cause of application malfunctions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional error analysis methods are used to identify root cause errors in application transactions, then the analysis process becomes increasingly complex and time-consuming, but the ability to accurately identify root cause errors deteriorates due to error masking and chain reactions
Solution Approach 1:
The patent segments the complex error analysis problem into distinct visual components: error flow diagrams that separate root cause errors from secondary errors, and organize errors by type and causation relationships. This segmentation allows analysts to view errors in discrete, manageable units rather than as an overwhelming mass of data, directly resolving the contradiction between analysis accuracy and process complexity
Solution Approach 2:
The patent introduces error flow diagrams as an intermediary visualization tool that mediates between raw error data and human analysis. These diagrams serve as a bridge that transforms complex error relationships into intuitive visual patterns, enabling accurate root cause identification without requiring analysts to manually navigate through complex error chains and masking relationships
2Loss of information
If detailed error data from multiple layers is collected for comprehensive analysis, then the completeness of error information improves, but the difficulty of detecting and measuring root cause errors increases due to the volume and complexity of data
Solution Approach 1:
The patent transforms error data from a flat, multi-layered structure into a visual dimension through error flow diagrams. By representing errors as graphical elements with spatial relationships, the system adds a visual dimension that makes hidden patterns and causation relationships visible, resolving the contradiction between information completeness and detection difficulty
Solution Approach 2:
The patent uses color coding to differentiate error types, severity levels, and causation relationships in the error flow diagrams. This visual encoding allows analysts to quickly distinguish between different categories of errors and identify patterns without manually examining detailed error data, reducing the difficulty of root cause detection while maintaining complete error information
3Loss of information
If manual analysis of error chains and masking relationships is performed, then the depth of error understanding improves, but the time required for analysis increases significantly
Solution Approach 1:
The patent performs preliminary automated analysis to generate error flow diagrams that pre-identify causation relationships and masking patterns before human analysis begins. This preliminary action of automatically organizing errors by type and relationship eliminates the need for manual tracing of error chains, providing both deep understanding and time efficiency simultaneously
Data Source
AI summary
Error data may be collected. The error data may represent a first plurality of errors of a first type and a second plurality of errors of a second type to occur in a plurality of instances of an application transaction. Visualization data may be generated. The visualization data may represent an error flow diagram to display on an output device. The error flow diagram may comprise a first block having a first visual property based on a first number of the first plurality of errors, a second block having a second visual property based on a second number of the second plurality of errors, and a first linkage between the first block and the second block.