Metadata Discrepancy Resolution via Dynamic Field Mapping
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Solution Overview
Problem
Metadata discrepancies between systems in data processing workflows can lead to improper validation and processing errors, especially when updates in metadata fields are not synchronized across subsystems, causing hardcoded metadata fields to mismatch with actual metadata formats.
Innovation Solution
A metadata discrepancy resolution tool that uses machine learning algorithms to identify and resolve mismatches in metadata fields during runtime by preventing processing until discrepancies are resolved, modifying expected metadata fields to match the actual fields, and updating mappings between metadata formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If metadata fields are hardcoded within applications to ensure consistent data processing, then processing reliability is improved, but adaptability to metadata structure changes deteriorates
Solution Approach 1:
The patent implements dynamic metadata field mapping that automatically adapts to structure changes. Instead of static hardcoded fields, the system uses runtime detection and dynamic mapping configurations that can adjust to new metadata formats without requiring application code changes, thus maintaining reliability while gaining adaptability
Solution Approach 2:
The system changes the parameter state of metadata field mappings from fixed to variable. By implementing configurable mapping parameters that can be modified at runtime based on detected metadata structures, the system maintains processing reliability through consistent validation while adapting to structural changes via parameter adjustments
2Quantity of substance
If a system is updated to generate output data with changed metadata fields, then data completeness is improved, but compatibility with downstream systems deteriorates
Solution Approach 1:
The patent introduces an intermediary metadata mapping layer between the first system and second system. This intermediary automatically detects metadata structure changes, creates appropriate mapping configurations, and translates between different metadata formats, ensuring both data completeness from updates and compatibility with downstream systems
Solution Approach 2:
The system performs preliminary metadata detection and mapping configuration before data processing occurs. By proactively identifying metadata structure changes and establishing mapping rules in advance, the system prevents compatibility issues before they affect downstream processing, maintaining both completeness and reliability
3Productivity
If hardcoded metadata fields are used to identify data columns, then processing speed is improved, but accuracy of data validation deteriorates when metadata structures change
Solution Approach 1:
The patent implements dynamic metadata field identification that maintains processing speed through optimized runtime detection while ensuring validation accuracy through adaptive mapping. The system dynamically determines the correct mapping between metadata fields and data columns based on actual metadata structures, preventing misalignment errors while maintaining efficient processing
Data Source
AI summary
A system includes first and second subsystems and a third processor. The first subsystem includes a first memory and a first processor. The first memory stores data, which includes metadata associated with transmitted metadata fields. The first processor transmits the data to the second subsystem. The second subsystem includes a second memory and a second processor. The second memory stores expected metadata fields. The second processor receives the data. The third processor determines that the first subsystem transmitted the data to the second subsystem and that a mismatch exists between the transmitted and expected metadata fields. In response, the third processor prevents the second subsystem from executing an application configured to process the data using the expected metadata fields. The third processor resolves the mismatch by modifying the expected metadata fields such that they correspond to the transmitted metadata fields and allows the second subsystem to execute the application.