Universal Data Mapping Pipeline for Application Migration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for data migration between different execution environments are time-consuming and costly due to the need for manual data mapping between varied data types, including structured, semi-structured, and unstructured data, which requires labor-intensive customization and lacks efficient automated solutions.
Innovation Solution
A universal data mapping pipeline utilizing a supervised machine learning system with a data classification module and inference module, employing neural network algorithms to automatically detect data types and generate candidate mappings, reducing the need for manual customization and improving mapping accuracy across diverse data types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data mapping methods are used between varied data types, then data migration can be performed, but the process becomes time-consuming and costly
Solution Approach 1:
The patent replaces manual mechanical data mapping processes with an automated machine learning system. The system uses supervised learning algorithms to automatically detect data types, generate candidate mappings, and transform data between structured, semi-structured, and unstructured formats, eliminating the need for time-consuming manual mapping while maintaining high accuracy through algorithmic intelligence
Solution Approach 2:
The data mapping system performs self-service by automatically detecting data types, generating candidate mappings without human intervention, and executing data transformations. The machine learning model learns from training data and autonomously improves its mapping accuracy over time, reducing dependency on manual customization and expert intervention
2Measurement precision
If manual customization is performed for data mapping, then mapping accuracy can be improved, but labor-intensive effort is required
Solution Approach 1:
The system changes the parameters of data mapping by using machine learning models with adjustable parameters that are optimized through supervised training. The system learns optimal mapping parameters from training data, automatically adapting to different data types and formats without requiring manual parameter tuning, thereby maintaining high accuracy while improving productivity
Solution Approach 2:
The system performs preliminary action by pre-training machine learning models on diverse data types and formats before actual data migration. The model learns mapping patterns and relationships in advance, so when production data needs to be mapped, the system can quickly generate accurate mappings without time-consuming manual customization, thus improving both accuracy and efficiency
3Productivity
If automated solutions are implemented for data mapping, then processing speed increases, but handling diverse data types becomes more difficult
Solution Approach 1:
The patent implements a universal data mapping system that can handle multiple data types including structured, semi-structured, and unstructured data through a single machine learning platform. The system uses multi-functional algorithms that automatically detect and adapt to different data formats, providing versatile data type compatibility while maintaining high processing speed through automated operations
Solution Approach 2:
The system employs dynamic adaptation mechanisms where the machine learning model automatically adjusts its behavior based on the input data type. The system dynamically selects appropriate processing strategies and transformation rules based on real-time data analysis, enabling it to efficiently handle diverse data types without sacrificing processing speed or requiring separate specialized systems for each data format
Data Source
AI summary
A system and method of operating an integration application management system comprising a processor executing code instructions for modelling, via a graphical user interface (GUI), a business integration process including a data mapping type visual element and a universal data type mapping pipeline system to classify first application input data via a classification module having a machine learning classifier to determine data classifications in the first application input data and to select, via an inference module, among a plurality of neural network mapping algorithms corresponding to each of the one or more data classifications. The system and method to generate, with the selected neural network mapping algorithm, and present to a user a plurality of sample data mappings for the data mapping type visual element from a first application to a second application in the business integration process.