Transaction Property Prevalence Differential Analysis
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Solution Overview
Problem
Modern web applications face difficulties in tracing the root cause of poor-performing transactions due to complex reports and alerts that do not adequately indicate the cause, and existing methods for troubleshooting slow transaction performance often dilute significant performance aberrations or fail to account for the prevalence of transaction properties.
Innovation Solution
A method that selects an overall set of end-user transactions, identifies an outcome-filtered subset, measures differences in transaction property prevalence between the subset and the overall set, and determines correlation factors based on these differentials to facilitate comparative performance analysis and root-cause identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional reporting methods are used to analyze transaction performance, then comprehensive data coverage is achieved, but the complexity of reports increases and root cause identification becomes difficult
Solution Approach 1:
The patent segments the overall transaction set into multiple outcome-filtered subsets based on different transaction outcomes (e.g., successful, failed, slow). Each subset is analyzed separately to identify correlation factors specific to that outcome. This segmentation transforms a single complex report into multiple focused analyses, making root cause identification easier while maintaining comprehensive coverage.
Solution Approach 2:
The patent extracts and isolates specific transaction properties that show significant prevalence differentials between outcome subsets. By taking out only the most relevant properties (those with high differentials) and focusing analysis on them, the system eliminates unnecessary complexity while preserving the ability to identify root causes. This extraction principle filters out noise and focuses attention on critical factors.
2Measurement precision
If all transaction properties are analyzed equally, then comprehensive analysis is performed, but significant performance aberrations are diluted by less significant data
Solution Approach 1:
The patent applies local quality by measuring and weighting transaction properties differently based on their prevalence differential. Properties that show significant differences between outcome subsets are given higher importance, while properties with minimal differences are downweighted. This differential weighting ensures that significant performance aberrations are not diluted by less significant data, as each property's contribution to the analysis reflects its actual relevance to the outcome being studied.
3Measurement precision
If traditional troubleshooting methods are used, then general overview is provided, but precise root cause identification for specific outcomes is achieved
Solution Approach 1:
The patent performs preliminary action by pre-calculating and storing the prevalence of each transaction property across different outcome subsets. When troubleshooting is needed, the system can quickly retrieve these pre-computed prevalence values and calculate differentials without re-analyzing all raw transaction data. This preliminary processing significantly reduces troubleshooting time while maintaining precise root cause identification capability.
Data Source
AI summary
In one embodiment, a method includes selecting an overall set of end-user transactions. The method further includes selecting an outcome. In addition, the method includes identifying an outcome-filtered subset of the overall set, the outcome-filtered subset comprising end-user transactions of the overall set associated with the selected outcome. The method also includes, for each transaction property of a plurality of transaction properties, measuring a difference between the transaction property's prevalence in the outcome-filtered subset and the transaction property's prevalence in the overall set. The measuring yields a property-prevalence differential for each transaction property. Moreover, the method includes, based, at least in part, on the property-prevalence differentials, determining one or more of the plurality of transaction properties to be correlation factors for the selected outcome.