Describe The Purpose Of Integrity Constraints And Identify T

Describe the purpose of integrity constraints and identify the main types of integrity constraints

Integrity constraints are essential rules in a database that ensure the accuracy, consistency, and reliability of the data stored within it. Their primary purpose is to enforce data integrity by restricting the types of data that can be entered or modified in a database system. This helps prevent the occurrence of invalid, inconsistent, or duplicate data, which could compromise the validity of the data analysis, reporting, and decision-making processes. These constraints act as safeguards that maintain data quality over time, especially in relational databases where multiple users may access and manipulate data concurrently.

The main types of integrity constraints on a logical data model include domain constraints, key constraints, entity integrity constraints, referential integrity constraints, and user-defined constraints. Each type plays a specific role in maintaining data integrity. Domain constraints specify the permissible values for each attribute, ensuring that data entered into a field adheres to a predefined set of acceptable values, such as data type, range, or format. For example, an attribute for a ZIP code might be restricted to five-digit numbers, ensuring consistency in address data.

Key constraints define uniqueness within a relation, most notably through primary keys, to ensure each tuple (row) is uniquely identifiable. For instance, a CustomerID or PartNumber must be unique for each record in their respective tables, preventing duplicate entries. Entity integrity constraints further reinforce this by ensuring that primary key attributes cannot be null, guaranteeing that each record can be uniquely identified.

Referential integrity constraints maintain consistent relationships between related tables by ensuring that foreign keys always refer to existing and valid primary keys in the related tables. For example, if a 'Parts' table references a 'Suppliers' table via a foreign key, referential integrity guarantees that every part's supplier exists in the suppliers' dataset. Lastly, user-defined constraints are custom rules set by the database designer to adhere to specific business rules or data policies, such as ensuring that the price of a part cannot be negative.

Part 2: Background

In the given scenario, tracking the price charged for each type of part, the supplier for each type, and the amount paid to the supplier (cost) requires a well-structured relational model. The relationship between parts and suppliers is one-to-many, where each supplier can supply many different parts, but each part is supplied by only one supplier. This relationship must be accurately modeled to ensure data integrity and efficient retrieval.

The data has been organized into a table called Parts, which includes fields such as Part, Price, Cost, Supplier, Street, City, State, Zip, and Telephone. This table consolidates the key information related to parts and suppliers. The table's purpose is to facilitate tracking of the various attributes associated with parts and their respective suppliers, including contact details and geographical location.

To review the table's normalization status, the definition of Second Normal Form (2NF) from Connolly's text is utilized. As per the textbook, 2NF is a relation that is in first normal form (1NF) and where every non-primary-key attribute is fully functionally dependent on the entire primary key. This means that non-key attributes should not depend on only part of a composite primary key if such a key exists.

Applying this definition to the provided table, we must determine if all the non-key attributes depend fully on the entire primary key. If the primary key comprises the Part attribute alone, then the question becomes whether all other fields are fully dependent on Part. If the primary key is a composite, such as a combination of Part and Supplier, then each non-key attribute must depend on both parts of that composite.

Analysis of the Table's Normalization

The table includes the fields Part, Price, Cost, Supplier, Street, City, State, Zip, and Telephone. If Part is designated as the primary key, then it should uniquely identify each row, and the other attributes should depend solely on Part. Assuming Part is the unique identifier, attributes like Price, Cost, and Supplier are directly related to each specific part. Geographic details such as Street, City, State, Zip, and Telephone are related to the Supplier, not the Part itself.

If this is the case, then the table violates the principles of normalization because it mixes attributes related to the part and the supplier in one table. This indicates the presence of partial dependency—where non-key attributes depend only on a part of a composite primary key—which is not permissible in 2NF. To achieve 2NF, the data should be split into separate tables: one for Parts (Part, Price, Cost, Supplier) and another for Suppliers (Supplier, Street, City, State, Zip, Telephone). This separation ensures that each non-key attribute depends entirely on its primary key and eliminates redundancy and update anomalies.

Conclusion

In summary, integrity constraints serve to uphold data quality by enforcing rules that prevent invalid or inconsistent data entry. The main types include domain, key, entity integrity, referential integrity, and user-defined constraints. Proper implementation of these constraints ensures reliable, accurate, and meaningful data in relational databases. Regarding normalization, the provided table is not in 2NF because it contains partial dependency issues. To conform with 2NF, the data should be reorganized into separate, related tables to eliminate redundancy and maintain data integrity. This process of normalization and constraint enforcement is fundamental to designing robust databases that support efficient data management and integrity.

References

• Connolly, T., & Begg, C. (2015). Database Systems, 6th Edition. Pearson Education.
• Fundamentals of Database Systems, 7th Edition. Pearson Education.
• Database System Concepts, 7th Edition. McGraw-Hill Education.
• Relational Database Design and Implementation. Morgan Kaufmann.
• An Introduction to Database Systems, 8th Edition. Pearson Education.
• Information & Management, 58(1), 103447.
• IBM Press.
• Data & Knowledge Engineering, 125, 101799.
• McGraw-Hill Education.
• International Journal of Computer Science and Information Security, 16(1), 256-262.