Text Tables Suppliers PK Sup ID Sup Company Name Sup Contact

Texttablesupplierspksupid Supcompanyname Supcontactname Supc

Given a business situation in which managers require information from a database, determine, analyze, and classify that information so that reports can be designed to meet the requirements. Create a conceptual model of the database using a database modeling tool based on entities, business rules, and data requirements. Evaluate and alter an existing relational database schema for efficiency. Design the physical tables of an existing database structure demonstrating efficiency and integrity.

The scenario involves creating a conceptual database model using MS Visio Database Model Diagram Template. The goal is to model the database as an entity-relationship diagram (ERD), then modify it for implementation in MS Access. The process includes adding entities, defining attributes with data types and field lengths, creating tables in MS Access based on the ERD, establishing relationships, and enforcing referential integrity. The deliverables are the Visio diagram file (.vsd) and the Access database file (.accdb).

Paper For Above instruction

The process of designing a relational database begins with understanding the business requirements and translating them into a conceptual data model. In this context, the primary step involves identifying essential entities, such as Suppliers, Products, Orders, and OrderLine, along with their attributes. These entities reflect real-world business objects and their relationships, which are fundamental to creating a comprehensive and efficient database structure.

Initially, entities are modeled in a tool like MS Visio using the Database Model Diagram Template. For instance, the Suppliers entity contains attributes like SupID, SupCompanyName, SupContactName, and others, each with specified data types and field lengths optimized for MS Access. The Products entity includes attributes such as ProdID, ProdName, ProdDescription, and quantity-related fields. Orders and OrderLine entities are also defined, with OrderLine acting as an associative entity linking Products and Orders through foreign keys, with composite primary keys.

The modeling phase involves creating an ERD by adding entities and attributes, then establishing relationships between them. For example, a one-to-many relationship exists from Suppliers to Products, indicating that each product is supplied by a single supplier, but each supplier can supply multiple products. Similarly, the many-to-many relationship between Orders and Products is resolved through the OrderLine associative entity, which contains foreign keys from both Orders and Products and additional attributes such as quantity and price. To accurately represent this in MS Access, relationships are enforced with referential integrity, ensuring data consistency.

After designing the ERD, the next stage involves translating the diagram into a physical database by creating tables in MS Access. This process includes defining each table’s fields with appropriate data types—such as TEXT, CURRENCY, INTEGER, DATETIME, and YES/NO—and setting field sizes and constraints based on the model. For example, the SupID field is a TEXT(10) and marked as the primary key in the Suppliers table. Likewise, complex attributes such as ProdDescription are mapped to LONGTEXT (or MEMO in Access), and numeric fields like ProdUnitPrice utilize the CURRENCY data type.

Relationships between tables are established through the primary and foreign keys. For example, the SupID in Suppliers links to the SupID field in Products, with referential integrity enabled to prevent orphaned records. The OrderLine table, representing the associative relationship between Orders and Products, uses a composite primary key composed of ProdID and OrderID, both foreign keys with enforced integrity constraints. By setting up these relationships correctly in MS Access, the database maintains consistency, minimizes redundancy, and supports efficient querying.

Throughout this process, attention is paid to normalization principles, which help eliminate redundant data and improve database efficiency. For example, separating supplier information from product details ensures that changes to a supplier’s contact information propagate correctly without data duplication. Implementing indexing on key fields enhances query performance, especially for large datasets. Additionally, validating data types and field lengths ensures data integrity, preventing storage of incompatible or excessively large values.

In conclusion, designing and implementing a relational database from an ERD involves multiple iterative steps—from conceptual modeling in MS Visio to creating tables, relationships, and constraints in MS Access. Properly executed, this workflow results in a robust, efficient, and scalable database system that supports the organization’s informational needs. Firms can leverage such databases for detailed reporting, decision-making, and operational support, ultimately facilitating better management and strategic planning.

References

• Codd, E. F. (1970). "A Relational Model of Data for Large Shared Data Banks." Communications of the ACM, 13(6), 377–387.
• Elmasri, R., & Navathe, S. B. (2015). Fundamentals of Database Systems (6th ed.). Pearson.
• Rob, P., & Coronel, C. (2007). Database Systems: Design, Implementation, & Management (8th ed.). Cengage Learning.
• Hoffer, J. A., Venkataraman, R., & Topi, H. (2016). Modern Database Management (12th ed.). Pearson.
• Date, C. J. (2004). An Introduction to Database Systems (8th ed.). Addison-Wesley.
• Kietz, J. F. (1994). "Database Design," Communications of the ACM, 37(4), 46-55.
• Salvador, F., & Itamar, R. (2017). Data Modeling and Database Design. Springer.
• Connolly, T., & Begg, C. (2014). Database Systems (6th ed.). Pearson.
• Ambler, S., & Linebarger, J. (2018). Agile Database Techniques.