Cross-Application Event Correlation for Software Trace Analysis
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Solution Overview
Problem
Current software platforms lack efficient methods to objectively evaluate and improve software applications based on user journeys, leading to inefficient resource utilization and suboptimal user experiences due to unclear performance metrics and lack of integration across applications.
Innovation Solution
The solution involves mining event data to generate global traces of user interactions across multiple software applications, using machine-learning techniques to link local traces and identify effective application usage, thereby determining resource utilization and suggesting improvements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple software applications are used to perform tasks, then task completion capability is improved, but resource utilization increases and user experience deteriorates due to unclear performance metrics
Solution Approach 1:
The patent segments user journey data into application-specific traces and correlates them across multiple applications. By dividing the analysis into individual application traces and then linking them through event correlation, the system can evaluate each application's performance separately while understanding their combined effect, thus managing complexity of multi-application systems.
Solution Approach 2:
The patent implements feedback mechanisms by analyzing correlated traces to identify underperforming applications and providing objective evidence about their performance. This feedback loop enables continuous improvement of application selection and ordering based on actual user journey data, optimizing resource utilization while maintaining task completion capability.
2Reliability
If software applications require more human interaction, then task completion accuracy is improved, but productivity decreases
Solution Approach 1:
The system uses feedback from correlated trace analysis to identify which applications require human interaction and at what stages. By objectively measuring performance across applications, the system can determine optimal ordering and selection to minimize human intervention while maintaining accuracy, thus improving productivity without sacrificing reliability.
3Power
If software applications with high computing resource utilization are used, then processing capability is improved, but resource efficiency deteriorates
Solution Approach 1:
The patent employs feedback from trace correlation analysis to objectively evaluate which high-resource applications are actually necessary for task completion. By identifying underperforming applications and their impact on resource utilization, the system can optimize application selection to achieve necessary processing capability with improved resource efficiency.
4Measurement precision
If user journeys are tracked across multiple applications, then performance evaluation accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the trace correlation process into manageable components: extracting application-specific traces, identifying correlation events, and linking traces through structured rules. This segmentation enables precise performance evaluation across applications while managing system complexity through modular processing steps.
Solution Approach 2:
The system uses event correlation as an intermediary mechanism to link traces across applications. By establishing structured correlation rules and using event-based mediation, the system achieves accurate cross-application performance tracking without direct complex integration between all applications, thus reducing overall system complexity.
Data Source
AI summary
An example embodiment may involve identifying local traces of related events within a plurality of event data repositories, wherein each of the event data repositories is respectively associated with a software application; using a clustering model, assigning the local traces into clusters; determining positive rules that define when pairs of the local traces are linked to a common global trace, and negative rules that define when the pairs are linked to different global traces; linking the pairs into global traces; iteratively training a similarity model to project the local traces into a vector space such that the pairs that are linked to common global traces exhibit a greater similarity with one another than the pairs that are linked to different global traces; and based on the similarity model as trained, linking further local traces to the global traces.