Database Consolidation via Metadata Synthesis and Conflict Resolution
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Solution Overview
Problem
Current database management methods, such as data integration and migration, face challenges in handling changes in business conditions like acquisitions and divestitures, leading to complexities in data consistency, latency, and increased operational costs due to disparate systems and business process inconsistencies.
Innovation Solution
A method for consolidating databases by synthesizing metadata, data values, business processes, and business rules into a single consolidated database, resolving conflicts, and standardizing configuration data to ensure relational integrity and consistent business processes, which involves creating temporary databases to match the destination database's metadata and data formats, and populating the consolidated database with resolved duplicates and consistent data values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data integration methods (EAI/SOA) are used to manage databases during business changes, then data can be transmitted between systems, but system complexity increases and operational costs rise due to hub-and-spoke architectures and loose coupling services
Solution Approach 1:
The patent merges multiple disparate databases into a single consolidated database, eliminating the need for complex EAI/SOA architectures. By consolidating data storage and management into one system, the patent removes the hub-and-spoke structure and loose coupling services, thereby reducing system complexity while maintaining data transmission and access capabilities.
Solution Approach 2:
The consolidated database serves as a universal data repository that handles all data storage, retrieval, and management functions that previously required multiple separate systems. This single database structure provides multi-functional capabilities, eliminating the need for specialized integration layers and reducing overall system complexity.
2Productivity
If data migration (ETL) is used to move data between databases, then data can be copied and transformed, but data consistency and completeness are compromised due to lack of validation during migration
Solution Approach 1:
The patent performs preliminary actions by establishing comprehensive validation rules and conflict resolution mechanisms before data migration occurs. The system pre-configures business rules, data quality standards, and consistency checks that automatically execute during the migration process, ensuring data integrity is maintained from the outset rather than requiring post-migration corrections.
Solution Approach 2:
The patent implements feedback mechanisms that continuously monitor data consistency during migration. The system validates data as it is transferred, provides real-time feedback on consistency issues, and automatically resolves conflicts based on predefined rules, ensuring data completeness and accuracy are maintained throughout the migration process.
3Adaptability or versatility
If disparate databases are maintained separately during acquisitions and divestitures, then business entity independence is preserved, but operational costs increase due to independent maintenance and business process inconsistencies
Solution Approach 1:
The patent segments the consolidated database into logical units or partitions that can be independently managed for different business entities. This segmentation allows maintaining business entity independence through isolated data sections while still benefiting from the efficiency of a unified database structure, reducing operational costs compared to fully separate database systems.
Solution Approach 2:
The patent applies homogeneity by standardizing data structures, formats, and business rules across all business entities within the consolidated database. This uniform approach eliminates inconsistencies between disparate systems, reduces maintenance complexity, and lowers operational costs while preserving the ability to manage different business entities through standardized interfaces.
Data Source
AI summary
Methods for consolidating databases while maintaining data integrity are disclosed. A source database and target database are compared, and consolidated, and the consolidated databases are used. In other examples, a database is split to support divested entities.