AI Graph-Based Simulation for Enterprise Upgrade Error Detection
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Solution Overview
Problem
Existing methods for detecting and correcting errors during application upgrades in computing systems are inefficient and time-intensive, often leading to undetected and uncorrected issues that negatively impact the application and the computing system.
Innovation Solution
A computing platform uses an AI engine trained on historical information to identify and correct errors by creating a simulated enterprise system, comparing system and virtual parameters, and executing corrective actions through a graphical database and AI engine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual review methods are used to detect and correct errors during application upgrades, then the process can be performed with simple tools, but the process becomes inefficient and time-intensive
Solution Approach 1:
The system performs preliminary actions by creating a simulated enterprise system before the actual application upgrade. This simulation environment allows the system to pre-test and detect potential errors before they affect the real enterprise system, enabling proactive error detection rather than reactive manual review after errors occur.
Solution Approach 2:
The patent creates a copy of the enterprise system as a simulated environment. This virtual copy allows automated testing and error detection without impacting the actual enterprise system operations. The simulation can be modified by upgrading a copy of the application, and system parameters are stored in a graphical database to enable automated comparison and error detection.
2Measurement precision
If comprehensive error detection is performed during application upgrades, then error accuracy improves, but the complexity of the system increases
Solution Approach 1:
The patent introduces an intermediary simulated enterprise system that mediates between the actual enterprise system and the upgrade process. This simulation layer acts as a buffer, allowing comprehensive error detection through automated parameter comparison without directly complicating the core enterprise system. The graphical database serves as another intermediary for storing and managing system parameters.
Solution Approach 2:
The system segments the enterprise system into manageable components by creating a simulated environment with virtual parameters that correspond to real system parameters. This segmentation allows automated detection of specific parameter differences (such as library versions) without requiring complex analysis of the entire enterprise system at once.
3Productivity
If automated AI-based error detection is implemented, then productivity improves, but the initial setup and training requirements increase complexity
Solution Approach 1:
The AI engine is designed to automatically detect errors by comparing system parameters from the simulated enterprise system with actual enterprise system parameters. The system self-services error detection without requiring manual intervention or complex configuration. The AI engine automatically identifies discrepancies between virtual and actual system states and can trigger appropriate error correction actions.
Data Source
AI summary
Aspects of the disclosure relate to upgrading an application within a simulated version of an enterprise system to detect and correct potential errors as a result of the upgrade. A computing platform may create a simulated version of the enterprise system by receiving metadata associated with the enterprise system, and converting the metadata into system parameters. Virtual parameters may be created by the computing system based on upgrading an application within the simulated version of the enterprise system. The computing system may determine errors caused by the application upgrade within the simulated version of the enterprise system based on differences between the system parameters and the virtual parameters. The computing platform may determine actions to correct the errors and input the results and feedback into an AI engine to further refine the accuracy and reliability of the computing platform over time.