ML Framework for Cloud Data Fusion Code Generation
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Solution Overview
Problem
Current methods for converting business requirements into code for cloud data fusion systems are manual, time-consuming, and inefficient, leading to incomplete or suboptimal code implementation and resource wastage.
Innovation Solution
A machine learning model is trained using natural language processing on business requirements and data fusion code data to generate a transformation map, which is then used to automatically generate and update code for the cloud data fusion system, ensuring complete and optimal transformation logic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to convert business requirements into code, then code implementation can be achieved, but the process is time-consuming and inefficient
Solution Approach 1:
The patent replaces manual mechanical code conversion processes with an automated machine learning-based system. The ML model automatically transforms business requirements into data fusion code, eliminating the need for manual analysis and coding, thereby dramatically improving productivity and reducing time consumption.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between business requirements and code generation. This intermediary learns the transformation patterns from training data and automatically generates code, serving as a smart mediator that bridges the gap between requirements and implementation without manual intervention.
2Reliability
If manual code generation methods are used, then code can be produced, but incomplete or suboptimal code implementation occurs
Solution Approach 1:
The patent performs preliminary training of the machine learning model using historical business requirements and corresponding code data. This preliminary action enables the model to learn comprehensive transformation patterns before actual code generation, ensuring that the generated code is complete and optimal without sacrificing productivity.
Solution Approach 2:
The patent implements a feedback mechanism where the system validates generated code and uses this information to continuously improve the machine learning model. This feedback loop ensures code completeness and quality while maintaining high generation efficiency through iterative optimization.
3Measurement precision
If manual code validation is performed, then code accuracy can be checked, but significant manual effort and time are required
Solution Approach 1:
The patent implements self-service validation where the machine learning model automatically validates its own generated code by checking against the transformation map and business requirements. This automated self-validation maintains high accuracy while eliminating time-consuming manual validation efforts.
Solution Approach 2:
The patent replaces manual code validation mechanics with automated machine learning-based validation. The system automatically checks code accuracy against transformation rules and requirements, providing precise validation results without manual intervention, thereby maintaining measurement precision while dramatically reducing validation time.
Data Source
AI summary
A device may receive business requirements data for implementing in a cloud data fusion system, and may process the business requirements data, with a machine learning model, to generate a transformation map for transforming the business requirements data. The device may generate code for the cloud data fusion system based on the transformation map, and may identify one or more differences between the code and previous code of the cloud data fusion system. The device may modify the code based on the one or more differences and to generate modified code, and may validate the modified code for transformation logic of the cloud data fusion system to generate final code. The device may perform one or more actions based on the final code.