Hierarchical Transaction Tree for Abnormal Loop Detection
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Solution Overview
Problem
In computing systems, identifying and addressing performance degradation issues caused by infinite or endless loops of transactions is challenging, requiring labor-intensive monitoring and manual analysis.
Innovation Solution
A hierarchical transaction tree is used to organize transactions, allowing for automatic detection of abnormalities based on tree size and depth level, with alerts generated to pinpoint potential root causes of performance degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual monitoring and analysis is used to identify performance degradation, then detection accuracy is improved, but labor intensity and time consumption increase
Solution Approach 1:
The system performs self-diagnosis by automatically detecting abnormal transaction loops through hierarchical tree analysis of transaction data, eliminating the need for manual monitoring while maintaining high detection accuracy through automated anomaly detection algorithms
Solution Approach 2:
The patent replaces manual mechanical analysis with automated computational analysis by constructing hierarchical transaction trees and using algorithms to detect abnormal loops, substituting human labor with machine-based automated detection systems
2Speed
If continuous monitoring is implemented to quickly identify performance issues, then response time is improved, but system complexity and resource consumption increase
Solution Approach 1:
The patent segments the monitoring system into hierarchical transaction trees that organize transactions by depth levels and parent-child relationships, allowing focused analysis of specific transaction paths rather than monitoring all transactions uniformly, thus reducing overall system complexity
Solution Approach 2:
The system applies different monitoring intensities to different transaction paths by identifying abnormal loops locally within the hierarchical tree structure, concentrating computational resources on problematic areas rather than uniformly monitoring all transactions
3Measurement precision
If manual analysis is used to pinpoint root causes, then analysis depth is improved, but productivity decreases
Solution Approach 1:
The system performs preliminary organization of transaction data into hierarchical trees with identified parent-child relationships and depth levels before analysis, pre-structuring the data to enable rapid root cause identification without requiring time-consuming manual exploration
Solution Approach 2:
The patent implements automated feedback mechanisms that detect abnormal transaction loops and immediately provide root cause information, creating a closed-loop system that continuously monitors and self-corrects without manual intervention, thereby increasing resolution speed
Data Source
AI summary
Examples relate to detecting an abnormality. The examples disclosed herein enable receiving, from a first user, a first request to perform a first transaction on at least one data record. A plurality of transactions originated from the first request may be organized in a first hierarchical tree-based data structure having multiple depth levels. The data structure may comprise a root node representing the first transaction and a leaf node representing a second transaction. The examples further enable detecting the abnormality based on at least one parameter where the at least one parameter comprises a size of the data structure and a depth level associated with the leaf node.