License Data Mapping System for Disparate Databases
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Professional records in disparate data sources and databases have varying data structures and formats, making it difficult to access and process them accurately and efficiently, particularly in updating license information and tracking changes, which requires an automated system for mapping and flagging database records.
Innovation Solution
A system and method for mapping licenses from disparate data sources, involving data structure analysis, cross-referencing license records with third-party information, flagging changes, and updating procedures, while providing a presentation of license information and status, including verification through government agencies or certification bodies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processing methods are used for disparate license data, then data accuracy can be maintained through human review, but processing efficiency and time consumption deteriorate significantly
Solution Approach 1:
The patent introduces an automated mapping system that acts as an intermediary between disparate license databases and the target system. This mapping system includes mapping templates, data transformation rules, and automated matching algorithms that bridge the gap between different data formats and structures, enabling efficient automated processing while maintaining accuracy through systematic validation mechanisms.
Solution Approach 2:
The system dynamically adjusts processing parameters based on the specific characteristics of each data source. Mapping templates can be configured with different transformation rules, validation thresholds, and matching criteria depending on the source database being processed. This allows the system to optimize processing efficiency for each specific data source while maintaining appropriate accuracy standards.
2Loss of time
If automated mapping systems are implemented, then processing efficiency improves, but the complexity of data structure analysis and cross-referencing increases
Solution Approach 1:
The system performs preliminary actions by pre-configuring mapping templates and establishing cross-reference relationships between different license databases before actual processing begins. Data structure analysis is conducted in advance to identify common patterns and relationships, which are then encoded into reusable mapping templates that accelerate subsequent processing without requiring complex real-time analysis.
Solution Approach 2:
The complex data processing task is segmented into distinct modular components: data extraction modules for different source types, mapping template engines, cross-reference matching engines, validation modules, and update modules. Each segment handles a specific aspect of the processing pipeline, making the overall complex system manageable and maintainable while enabling parallel processing to reduce time loss.
3Reliability
If regular updates and tracking of license changes are implemented, then data reliability improves, but the complexity of monitoring and updating procedures increases
Solution Approach 1:
The system implements feedback mechanisms where processed license data is continuously monitored for changes, and update notifications are automatically generated when discrepancies or expirations are detected. The mapping system compares current data against historical records and triggers automated update procedures when changes are identified, ensuring reliable current information without requiring manual intervention for each change.
Solution Approach 2:
The system performs self-service by automatically detecting license changes, generating update records, and maintaining current status information without requiring external intervention. The tracking mechanism autonomously monitors expiration dates, license status changes, and data consistency across sources, updating the database automatically when changes are detected, thereby maintaining high reliability with minimal operational complexity.
Data Source
AI summary
A system and method for mapping licenses from disparate data sources and databases from third parties triggered by a system registration request, analyzing data structures for license information records and third-party information records, cross-referencing a license record with a third-party information record, flagging database records that reflect a license field record change, updating mapping-related procedures and queries, and providing a presentation of license information records and related status.