Data Transformation Using Format Fingerprinting
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Solution Overview
Problem
Existing data transformation methods face challenges in uniformly converting heterogeneous source data into a consistent target format, requiring manual effort and insufficient data transformation examples to generate generalized program code, which is inefficient and prone to errors.
Innovation Solution
A method that automatically generates one-to-one mappings between source and target data formats using regular expressions to transform data, creating graphs of transformation paths and selecting optimal paths for uniform data conversion, thereby facilitating the development of generalized data transformation program code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual data transformation methods are used, then data conversion can be performed, but the process requires significant manual effort and is prone to errors
Solution Approach 1:
The system automatically generates transformation code by analyzing source and target data formats itself, without requiring manual intervention. The code generation module extracts format patterns from sample data and automatically creates transformation logic, enabling the system to serve itself rather than relying on manual programming.
Solution Approach 2:
The patent replaces manual mechanical data transformation operations with an automated code generation system. Instead of manually writing transformation code, the system uses algorithms to analyze data formats and generate transformation code automatically, substituting human effort with computational processes.
2Adaptability or versatility
If insufficient data transformation examples are used, then generalization is difficult, but collecting more examples increases data processing complexity
Solution Approach 1:
The system extracts essential format patterns from data samples by identifying and isolating the core structural elements that define data formats. Rather than processing entire datasets, the extraction module pulls out only the relevant format-defining characteristics, reducing complexity while maintaining generalization capability.
Solution Approach 2:
The system changes the parameters of data representation by transforming raw data samples into abstracted format patterns. This parameter transformation allows the system to work with generalized format descriptors rather than specific data instances, enabling broader adaptability with reduced processing complexity.
3Productivity
If automated code generation is implemented, then productivity increases, but the complexity of the transformation system increases
Solution Approach 1:
The system segments the code generation process into distinct functional modules: format analysis, pattern extraction, code generation, and validation. This segmentation allows each module to handle a specific aspect of the transformation task, improving overall productivity while managing complexity through modular design.
Solution Approach 2:
The code generation system is designed as a universal platform that can handle multiple data formats and transformation scenarios through a single integrated architecture. This multi-functionality approach increases productivity by eliminating the need for separate manual coding for each transformation type, while the underlying universality helps manage complexity through standardized processes.
Data Source
AI summary
A method, computer program product, and computer system for transforming sets of source data having different formats into respective sets of target data having a same format. N source patterns are determined and respectively describe N different formats in which N sets of source data items are formatted, where N≥1. A target format pattern is determined and describes a target format in which a target data items are formatted. N graphs are generated and respectively describe transformations of the N source patterns to the target pattern. Each graph includes multiple transformation paths. Each transformation path transforms the source pattern to the target pattern in a manner that maps source strings in the source pattern to each target string in the target pattern. A single transformation path is selected from the multiple transformation paths resulting in N single transformation paths having been selected.