ML-Based Integration Validation for Enterprise Application Optimization
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Solution Overview
Problem
Poorly designed integration and process flows in enterprise application development lead to adverse impacts on application development time, runtime performance, and message quality, with challenges exacerbated by the involvement of multiple vendors and legacy systems.
Innovation Solution
Utilization of machine learning techniques to syntactically and semantically validate integration and process components, predicting runtime outcomes and recommending optimized configurations for improved application development and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual validation methods are used for integration and process flows, then developers can identify and correct issues, but the process is time-consuming and delays time to market
Solution Approach 1:
The patent applies preliminary action by performing validation of integration and process flows during the design phase rather than during testing or deployment. The system automatically validates configurations, detects anomalies, and provides feedback before the application is deployed, thereby maintaining validation accuracy while significantly reducing time to market by eliminating post-deployment debugging cycles
Solution Approach 2:
The patent replaces manual mechanical validation processes with automated machine learning-based validation systems. The ML models automatically analyze configuration data, detect anomalies, and validate integration flows without human intervention, maintaining high validation accuracy while eliminating the time-consuming manual review process that delays time to market
2Loss of time
If integration and process flows are not properly validated, then time to market is reduced, but runtime performance errors increase and message quality deteriorates
Solution Approach 1:
The system performs preliminary validation of integration configurations and process flows before deployment using machine learning models. This early detection and correction of anomalies ensures runtime performance reliability is maintained while enabling rapid time to market by preventing performance issues before they occur in production
Solution Approach 2:
The patent implements feedback mechanisms where the ML validation system provides real-time feedback on configuration anomalies and potential performance issues. This feedback loop allows developers to correct problems during design, ensuring runtime performance reliability is maintained while enabling rapid deployment by preventing performance degradation
3Reliability
If comprehensive validation of integration components is performed, then message quality and durability improve, but system complexity increases
Solution Approach 1:
The patent applies self-service by enabling the validation system to automatically analyze configuration data, detect anomalies, and generate recommendations without requiring complex manual validation processes. The ML models perform comprehensive validation of integration components, ensuring message quality and durability while keeping the system relatively simple by automating what would otherwise require complex manual procedures
Solution Approach 2:
The patent implements a universal ML-based validation platform that handles multiple types of integration components and validation scenarios through a single system. This multi-functional approach ensures comprehensive validation for message quality and durability while reducing overall system complexity by consolidating multiple validation functions into one unified ML system
4Adaptability or versatility
If enterprise SMEs manually validate integrations involving multiple vendors and legacy systems, then customization is possible, but productivity decreases and errors increase
Solution Approach 1:
The patent replaces manual SME validation processes with automated ML validation systems that can handle multiple vendors and legacy systems. The ML models are trained to recognize patterns and validate diverse integration scenarios, maintaining customization capability through configurable validation rules while dramatically increasing productivity by eliminating time-consuming manual validation of complex multi-vendor integrations
Solution Approach 2:
The patent utilizes parameter changes by adjusting ML model parameters and validation thresholds based on specific vendor requirements and legacy system characteristics. This allows the system to maintain customization capability for different integration scenarios while achieving high productivity through automated validation, eliminating the need for manual customization for each vendor or system
Data Source
AI summary
A method comprises receiving configuration data comprising a plurality of parameters of at least one computing environment. One or more of the parameters correspond to integration of one or more elements of the least one computing environment with one or more other elements at least one of within and external to the least one computing environment. In the method, the parameters are analyzed to detect one or more anomalies in the configuration data, and the configuration data and the one or more detected anomalies are inputted to one or more machine learning models. The method also comprises determining, using the one or more machine learning models, one or more modifications to at least one of the plurality of parameters based on the inputted configuration data and one or more detected anomalies, and transmitting the determination comprising the one or more modifications to a user over a communications network.