Token Mapping Data Transformation Code Generation
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Solution Overview
Problem
Current data transformation programs face challenges in selecting appropriate transformation models and understanding the transformation process, particularly with machine learning models, where accuracy and understanding of transformations are difficult to achieve without extensive training data and user involvement.
Innovation Solution
A computer system identifies mappable tokens in input strings and creates initial and user mappings to generate program code that transforms input strings to output strings, allowing for data transformation with user input and reduced need for diverse examples, thereby increasing accuracy and user understanding of the transformation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning models are used for data transformation, then transformation capability is improved, but accuracy and understanding of transformations are difficult to achieve without extensive training data and user involvement
Solution Approach 1:
The patent segments the data transformation process into distinct components: token identification, mapping generation, and code synthesis. This segmentation allows each component to be optimized independently, improving overall accuracy without requiring extensive training data for the entire system.
Solution Approach 2:
The system performs preliminary actions by automatically generating initial mappings and token identifications before user review. This preliminary processing reduces the amount of user involvement needed while maintaining high accuracy, as users only need to review and confirm rather than create everything from scratch.
2Measurement precision
If machine learning models are trained with extensive training data, then accuracy is improved, but complexity of the transformation process increases
Solution Approach 1:
The system performs self-service by automatically generating mappings, identifying tokens, and creating transformation code without requiring extensive user input or manual configuration. This automation maintains high accuracy while reducing the perceived complexity for users.
Solution Approach 2:
The system incorporates feedback mechanisms where user confirmations and corrections are used to refine and improve the generated mappings. This feedback loop allows the system to achieve high accuracy through iterative improvement rather than requiring extensive initial training data.
3Ease of operation
If user involvement is reduced in data transformation, then ease of operation is improved, but understanding of the transformation process deteriorates
Solution Approach 1:
The patent introduces an intermediary layer that generates human-readable explanations and visualizations of the transformation process. This intermediary maintains ease of operation by automating the process while preserving user understanding through clear documentation and explanations of what transformations are being applied.
4Measurement precision
If extensive training data is required for machine learning models, then transformation accuracy is improved, but loss of time in data preparation increases
Solution Approach 1:
The system performs preliminary actions by automatically generating initial mappings and token identifications from the provided data transformation example. This preliminary processing achieves high accuracy without requiring extensive time-consuming data preparation, as the system leverages the single provided example to generate the transformation logic.
Data Source
AI summary
A computer implemented method transforms data. Responsive to receiving a data transformation of an input string to an output string, a computer system identifies mappable tokens in the input string that are mappable to the output string. The computer system creates a set of initial mappings for a set of common tokens in the mappable tokens. The set of initial mappings maps the set of common tokens from the input string to the output string. The computer system creates a set of user mappings that maps the mappable tokens from input string to the output string using a user input to the set of initial mappings. The computer system generates program code that transform input strings to output strings using the set of user mappings that maps the mappable tokens from input string to the output string, wherein the program code is used to transform input strings to output strings.