Data Conversion Framework Using Succession Graphs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data conversion methods require significant user intervention and are inefficient in automatically converting data between different data formats and storage schemas.
Innovation Solution
A computer-implemented process that populates data records in source and target systems with sample data, generates succession graphs, and determines parameters for CONCATENATE or EXTRACT functions to automatically convert data between different data storage schemas, minimizing user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data conversion methods are used, then conversion accuracy can be maintained through user review, but user intervention time and conversion complexity increase significantly
Solution Approach 1:
The system performs self-service by automatically analyzing correspondences between data records and deriving conversion functions without requiring user intervention. The machine automatically compares data formats, identifies mapping relationships, and generates executable conversion functions, eliminating the need for manual review while maintaining accuracy through automated validation.
Solution Approach 2:
The patent replaces manual mechanical analysis with automated machine analysis. Instead of users manually reviewing and verifying data correspondences, the system uses automated algorithms to analyze data records, identify patterns, and derive conversion functions, substituting human cognitive processes with computational mechanisms.
2Productivity
If automated conversion functions are derived without user intervention, then conversion speed increases, but conversion reliability may decrease due to lack of manual verification
Solution Approach 1:
The system incorporates feedback mechanisms where the automated derivation process continuously validates derived conversion functions against the analyzed data correspondences. The machine analyzes multiple data records, tests derived functions, and refines conversions based on validation results, creating a closed-loop system that ensures reliability without manual intervention.
Solution Approach 2:
The system performs preliminary analysis of data correspondences before finalizing conversion functions. By pre-analyzing multiple sample records and deriving conversion rules in advance, the system establishes reliable conversion patterns that can be confidently applied without subsequent manual verification, ensuring both speed and reliability.
3Manufacturing precision
If complex data format transformations are performed manually, then handling of edge cases improves, but overall conversion efficiency decreases
Solution Approach 1:
The system applies partial action by focusing automated analysis on critical data correspondences and conversion patterns. Instead of manually reviewing every possible edge case, the machine automatically identifies and handles relevant edge cases through pattern recognition across multiple data records, providing sufficient coverage without requiring exhaustive manual verification of every potential scenario.
Data Source
AI summary
An enhanced data conversion framework, in which a data record in each of first and second data sources is populated with manually selected, representative sample data, the first and second data sources using different data storage schemas to store the representative sample data as instance values of instance elements. Parameters for a CONCATENATE function or an EXTRACT function are automatically determined based on a selected succession graph, and non-sample data is converted between the different data storage schemas of the first and second data sources, using the CONCATENATE function or the EXTRACT function.