Integration Process Heatmap for Predictive Performance Detection
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Solution Overview
Problem
Novice users face challenges in constructing integration processes in low-code environments due to the lack of tools for preemptively visualizing performance issues, leading to trial and error and potential operational disruptions.
Innovation Solution
A heatmap is generated within a graphical user interface to visually represent the performance of integration processes, using a predictive model to map performance metrics to colors on a virtual canvas, allowing users to identify and resolve issues before deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a low-code integration environment is used to simplify business processes, then ease of operation is improved, but the ability to preemptively detect performance problems deteriorates
Solution Approach 1:
The system performs preliminary analysis of integration processes during the design phase, generating performance predictions and visualizing potential issues before deployment. The predictive model analyzes the integration process design and identifies performance problems in advance, allowing users to correct issues before they affect production operations.
Solution Approach 2:
The system uses color-coded visual indicators (heatmap) to represent performance metrics and potential problems in the integration process. Different colors indicate different performance levels or issue severities, making it easy for users to quickly identify and locate performance problems without requiring technical expertise in reading logs or metrics.
2Measurement precision
If traditional development tools are used in code-heavy integration environments, then measurement precision is improved, but ease of operation deteriorates due to coding expertise requirements
Solution Approach 1:
The system introduces an intermediary predictive model that bridges the gap between complex performance analysis and user-friendly presentation. The model handles the sophisticated analysis internally while presenting simplified visual results to users, eliminating the need for users to understand complex performance metrics or coding concepts.
Solution Approach 2:
The system creates a virtual representation of the integration process performance through visualization overlays that mirror the actual process structure. The heatmap overlay copies the process layout and adds performance information, allowing users to understand performance issues in the context of the visual process design rather than abstract metrics.
3Adaptability or versatility
If integration processes are constructed through trial and error, then adaptability is improved for novice users, but loss of time increases due to multiple iterations
Solution Approach 1:
The system provides immediate feedback during the design phase by visualizing predicted performance issues as users construct or modify integration processes. This real-time feedback allows users to understand the impact of their design choices and make corrections before deployment, eliminating the need for time-consuming trial-and-error iterations after deployment.
Solution Approach 2:
The system takes preliminary anti-action by identifying and highlighting potential performance problems before they can cause operational disruptions. By showing users where performance issues are likely to occur during the design phase, the system prevents these problems from manifesting in production, thereby preventing the need for corrective iterations.
4Reliability
If performance problems are detected after deployment, then reliability of post-deployment monitoring is improved, but productivity deteriorates due to operational disruption
Solution Approach 1:
The system performs performance analysis and problem detection during the design phase before deployment, rather than relying solely on post-deployment monitoring. By identifying and allowing correction of performance issues beforehand, the system ensures higher reliability of the deployed process while maintaining continuous productivity without operational disruptions.
Data Source
AI summary
Conventional problem detection for integration processes in an integration platform is inefficient and requires significant expertise. Disclosed embodiments generate a heatmap as an overlay over the components of an integration process, represented on a virtual canvas. The heatmap may comprise a color map with color regions that each represents the value of one or more predicted and/or actual performance metrics for the components of the integration process overlaid with that color region. Examples of performance metrics include the number of errors, the severity of errors, data throughput, bandwidth utilization, data volume, processing time, and/or the like. The heatmap may comprise a plurality of levels of resolution that may be transitioned between by zooming in and out of the virtual canvas. Higher levels of resolution may comprise indicators conveying additional information about the performance of the corresponding areas.