Merging relations is the process of integrating multiple database schemas, often derived from different Entity-Relationship (E-R) Diagrams, into a single, unified schema.
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Key Steps in Merging Relations:
1.) Identifying and Resolving Conflicts:
- This step involves detecting and addressing inconsistencies between schemas that may arise due to differences in naming conventions, attribute types, constraints, or structural variations.
Types of Conflicts and Their Resolutions:
Naming Conflicts:
- When entities or attributes have different names but represent the same concept (e.g., CustomerID vs. ClientID).
- Solution: Standardize naming conventions by selecting a common name.
Structural Conflicts:
- Differences in schema structure, such as one schema storing CustomerAddress as a separate entity while another includes it as an attribute.
- Solution: Choose a design that minimizes redundancy and aligns with normalization principles.
Data Type Mismatches:
- When the same attribute has different data types (e.g., PhoneNumber as VARCHAR in one schema and INTEGER in another).
- Solution: Select a uniform data type that supports all use cases.
Key Conflicts:
- When primary keys differ across schemas for the same entity.
- Solution: Adopt a common primary key, possibly by mapping or transforming existing keys.
2.) Ensuring Consistency and Integrity:
After resolving conflicts, the merged schema must maintain data consistency, integrity, and referential correctness.
Ensuring Schema Integrity:
- Primary and Foreign Key Alignment: Ensure that relationships between entities remain intact after merging.
- Constraints and Business Rules: Maintain existing constraints (e.g., NOT NULL, UNIQUE, CHECK) to prevent data inconsistencies.
- Normalization and Denormalization: Apply normalization rules to eliminate redundancy while considering denormalization for performance optimization.
3.) Optimizing for Performance and Scalability:
After merging, the schema should be optimized to handle large-scale data efficiently.
Optimization Techniques:
- Indexing: Implement indexes on frequently searched attributes to speed up queries.
- Partitioning: Distribute large tables across multiple storage units to improve query performance.
- Data Caching: Use caching strategies for frequently accessed data.
- Query Optimization: Optimize SQL queries by restructuring joins and subqueries for better efficiency.