| | 75 | |
| | 76 | = Conclusion = |
| | 77 | |
| | 78 | Phase 6 demonstrates advanced relational database analysis through: |
| | 79 | |
| | 80 | * Complex SQL Construction: |
| | 81 | * multi-table joins |
| | 82 | * aggregate functions |
| | 83 | * conditional calculations |
| | 84 | * temporal calculations |
| | 85 | |
| | 86 | * Formal Relational Algebra Expression: |
| | 87 | * mathematical representation of query semantics |
| | 88 | |
| | 89 | * Procedural Encapsulation: |
| | 90 | * PostgreSQL stored procedures |
| | 91 | * reusable database-side reporting logic |
| | 92 | * parameterized analytical procedures |
| | 93 | |
| | 94 | * Empirical Validation: |
| | 95 | * sample data insertion |
| | 96 | * query execution results |
| | 97 | * calculation verification |
| | 98 | |
| | 99 | The three analytical scenarios provide comprehensive Wedding Planner insights: |
| | 100 | |
| | 101 | * Budget vs Actual Expenditure Analysis |
| | 102 | * Venue Capacity Utilization Analysis |
| | 103 | * Event RSVP Conversion Rate Analysis |
| | 104 | |
| | 105 | Together, these reports illustrate how a normalized relational database can support not only transactional operations, but also analytical reporting, operational decision-making, and performance-oriented database design. |
| | 106 | |
| | 107 | == Final Notes == |
| | 108 | |
| | 109 | All analytical procedures, SQL queries, and relational algebra expressions were tested using PostgreSQL 15. |
| | 110 | |
| | 111 | The implementation demonstrates how relational databases can support both transactional processing and analytical decision-making within a real-world Wedding Planner Management System. |