Final Project: Create A Database System

Page 1individual Final Project Create A Database Systempurpose To

Develop a comprehensive database system for MovinOn Inc., a moving and storage company operating across California, Nevada, and Arizona. The system should facilitate data sharing across multiple warehouses, manage personnel, vehicles, jobs, customers, storage units, and related operations. Design the database following normalization principles (3NF), create entity-relationship diagrams with proper cardinalities, and implement the database using SQL with sample dummy data. Generate reports such as outstanding balances, driver payments, job details, revenues, and available storage units. Justify design choices, validate the model, and provide a detailed explanation of normalization importance, validation process, challenges faced, and advice for future students.

Paper For Above instruction

The development of an efficient and reliable database system is crucial for companies like MovinOn Inc., which handle complex operations involving moving services, storage, personnel, vehicles, and customer data across multiple locations. Creating a well-structured database not only streamlines operations but also enhances data sharing, accuracy, and decision-making capabilities, especially in the context of expanding operations through mergers.

Firstly, understanding the business processes and data requirements is essential. MovinOn Inc. manages various entities such as customers, employees, drivers, vehicles, warehouses, storage units, job orders, and job details. Each of these entities possesses specific attributes; for instance, customers have contact details, employees and drivers have personal and employment-related information, vehicles are identified by license plates and specifications, whereas warehouses and storage units are geographically and physically defined. A comprehensive analysis leads to the identification of primary keys and foreign keys, ensuring referential integrity and normalization up to 3NF (Third Normal Form).

The process begins with normalization, which is vital to eliminate redundancy, dependency issues, and to create a flexible, scalable database. For example, customer data can be stored in a Customer table; employee and driver data in separate tables but linked via role identifiers. Vehicles are tracked in a Vehicle table, linked to job details and driver assignments. Warehouses are identified uniquely by their state abbreviation and number, with storage units linked to each warehouse. Job orders are connected to customers, personnel, and vehicles, with detailed records capturing mileage, weights, and other specifics.

An Entity-Relationship (ER) diagram visually represents these entities and their relationships. A warehouse is associated with multiple employees, and employees work at one warehouse. Drivers are contracted and their performance ratings influence billing, which necessitates a DriverRating table. Vehicles are assigned to jobs and linked via foreign keys; job details include references to vehicles, drivers, and job specifics. Storage units are leased to customers with start and end dates; unleased units are identified as available. Each relationship line indicates cardinalities such as one-to-many or many-to-many, with explicit notation for weak entities if applicable.

Once the ER diagram is validated for completeness and normalization, detailed metadata for each entity is documented. For example, the Customer table includes fields like CustomerID (PK), Name, ContactNumber, Address, and other attributes with specified data types (e.g., VARCHAR(100)). Similarly, the Vehicle table includes VehicleID, LicensePlate, Type, NumberOfAxles, Color, with clear descriptions. These metadata tables serve as blueprints for SQL table creation scripts, which enforce data types, constraints, default values, and validation rules.

Implementing the database involves writing SQL Data Definition Language (DDL) scripts that create tables, define primary keys, foreign keys, and constraints, as well as inserting dummy data for testing. For example, the Customer table is populated with sample customer records, and placeholder entries are created for vehicles, warehouses, employees, drivers, storage units, and job orders. Sample queries are then executed to verify data integrity and demonstrate functionality, such as listing all drivers and their assigned jobs, or calculating outstanding balances and revenues for a specific period.

Furthermore, a database diagram is generated using tools like SQL Server Management Studio (SSMS), illustrating relationships and cardinalities. This visual aids in understanding the data structure and validates the logical design. Reports are crafted using SQL SELECT statements combining data across multiple tables: e.g., a report on total amount due from each customer, including moving and storage charges; a list of driver payments considering safety ratings and performance fees; and availability status of storage units.

In addition to technical implementation, advocating for normalization emphasizes its importance in reducing redundancy, preventing update anomalies, and improving data consistency. Validating the model involves reviewing entity relationships, constraints, and test data queries, ensuring the system can respond accurately to real-world scenarios. Challenges faced during the project may include designing for flexibility amidst evolving business operations and managing complex relationships. Advice to future students includes thorough initial planning, detailed documentation, and iterative validation to build robust databases.

References

• Elmasri, R., & Navathe, S. B. (2015). Fundamentals of Database Systems (7th ed.). Pearson.
• Date, C. J. (2003). An Introduction to Database Systems (8th ed.). Addison-Wesley.
• Connolly, T., & Begg, C. (2014). Database Systems (6th ed.). Pearson.
• Harrington, J. L. (2016). Relational Database Design Clearly Explained. Morgan Kaufmann.