Automated Data Mapping via Metadata Comparison
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Solution Overview
Problem
The manual process of mapping data items from a source system to a target system in a data warehouse is time-consuming and requires significant user coordination due to differences in names, values, and rules between the two systems, necessitating an automated solution for data transfer.
Innovation Solution
A system and method for dynamically linking data from a source data structure to a target data structure using metadata attributes such as name, semantic type, and data type to automatically match and transform data fields, eliminating the need for user intervention and enabling efficient data transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual mapping of data items is used from source system to target system, then data transfer accuracy can be maintained, but significant user coordination and time are required
Solution Approach 1:
The system enables self-service automated mapping by comparing metadata attributes (names, data types, semantic types) between source and target systems to automatically determine mappings without user intervention. The computer compares metadata from the source data structure with metadata from the target data structure and automatically assigns mappings based on similarity metrics.
Solution Approach 2:
The system changes the approach from manual parameter matching to automated parameter comparison by evaluating multiple metadata attributes simultaneously (name similarity, data type compatibility, semantic type matching) and using weighted scoring to determine optimal mappings, thereby reducing time while maintaining accuracy.
2Adaptability or versatility
If manual mapping process is used, then complex data structure transformations can be handled, but significant user coordination is required
Solution Approach 1:
The system performs self-service by automatically comparing metadata attributes and determining mappings without user intervention. The computer autonomously evaluates name similarity, data type compatibility, and semantic type matching to handle complex transformations while requiring minimal user coordination.
Solution Approach 2:
The system provides universal automated mapping capability that handles various data structure transformations through a single unified process. The same metadata comparison and scoring mechanism works across different data types, structures, and transformation scenarios, eliminating the need for specialized manual coordination for each case.
3Productivity
If automated mapping is implemented using metadata comparison, then data transfer time is reduced, but mapping accuracy for complex transformations may be compromised
Solution Approach 1:
The system evaluates multiple parameters simultaneously (name similarity, data type compatibility, semantic type matching) and combines them using weighted scoring. This multi-parameter approach maintains mapping accuracy for complex transformations while enabling automated high-speed processing, achieving both productivity and precision.
Solution Approach 2:
The system replaces manual mechanical mapping processes with automated computational metadata comparison. The computer efficiently processes and compares metadata attributes at high speed while using sophisticated scoring algorithms to maintain accuracy, substituting human coordination with automated intelligent processing.
Data Source
AI summary
Provided are systems and methods for linking source data fields to target inputs having a different data structure. In one example, the method may include receiving a request to load a data file from a source data structure to a target data structure, identifying a plurality of target inputs of the target data structure, wherein the plurality of target inputs include a format of the target data structure, and at least one of the target inputs has a format that is different from a format of a source data structure, dynamically linking the plurality of source data fields to the plurality of target inputs based on metadata of the plurality of source data fields, and loading the data file from the source data structure to the target data structure.