ISQA 3310 - Managing The Database Environment Assignment 3

ISQA 3310 – Managing the Database Environment Assignment 3: Normalization

Identify the functional dependencies in the table and convert to a relation in 1NF (i.e., identify the primary key for the table). Using the functional dependencies, decompose the relation into a set of relations (relational tables) that meet 3NF requirements. Draw an ER model of the relations using IE (crow’s foot) notation, showing primary keys, foreign keys, and referential integrity constraints. Evaluate and improve the initial ER model for the parking tickets by identifying all functional dependencies, including transitive dependencies, and decompose into 3NF. Use ERwin to depict the final schema with appropriate integrity constraints. Reflect on challenges faced, resolutions, and principles learned during tasks 1 and 2.

Paper For Above instruction

The process of normalization is crucial in designing efficient and reliable databases. It involves analyzing relationships among data attributes, establishing functional dependencies, and decomposing tables to meet certain normal forms, primarily 1NF, 2NF, and 3NF. This paper discusses the detailed steps undertaken to normalize a student grade report table and a parking tickets database, providing insights into practical challenges and lessons learned during the process.

Normalization of Student Grade Report

Initially, the provided dataset consists of multiple attributes such as student ID (st_ID), student name (st_name), campus address, major, course ID, course title, instructor name, instructor location, and grade. The primary step involves identifying the functional dependencies. It is reasonable to assert that:

• st_ID functionally determines st_name, campus_address, and st_major, since each student has a unique ID and corresponding attributes.
• course_ID determines course_title, and possibly course_title determines course_ID, although in most databases, course_ID would be the primary key.
• inst_name functionally determines inst_location, as each instructor is associated with a unique location.
• The combination of st_ID and course_ID forms the primary key for the initial table, considering each student’s grade for each course.

With these dependencies, the table is converted to 1NF, where atomicity is maintained, and the primary key is identified as (st_ID, course_ID). This step ensures each attribute contains indivisible values.

Next, to reach 3NF, the relation needs to be decomposed further:

• A Student entity table includes st_ID, st_name, campus_address, and st_major.
• A Course entity table contains course_ID, course_title, and inst_name.
• An Instructor entity table with inst_name and inst_location.
• A Grade report table with st_ID, course_ID, and grade, referencing the Student and Course tables via foreign keys.

This decomposition removes transitive dependencies; for instance, inst_location depends only on inst_name, separating instructor details from course offerings.

In the design phase, these relations are modeled in ER diagrams using IE notation. Each entity has primary keys, and foreign keys link the related tables, with referential integrity constraints enforced to maintain data consistency. ERwin tools facilitate visual representation, ensuring that all PKs and FKs are displayed, and relationship verb phrases accurately describe their association.

Improving the Parking Tickets Database

Initially, the parking tickets database comprises a ParkingFine entity with attributes like ticket_no, stID, lName, fName, PhoneNo, State, Lic, LicNo, Code, Violation, and Fine. The primary key is ticket_no, and it captures details about each parking violation.

Analyzing the functional dependencies, we observe that:

• ticket_no determines all other attributes, including student info, license details, violation code, and fine.
• ticket_code (or code) indicates specific violation type and associated fine, hinting at a dependency where violation details are stored separately.
• Student information such as stID, lName, and fName are associated with the ticket, illustrating a dependency where students may have multiple tickets.

The transitive dependencies, especially between violation code and fine amount, suggest normalization to eliminate redundancies. Here, the relation can be decomposed into:

• A Student entity with stID, last name, first name, phone number, and state license details.
• A Violation entity capturing violation code, description, and fine amount.
• A ParkingTicket entity referencing the student and violation via foreign keys, with ticket_no as the primary key.

Using ERwin, these entities are diagrammed with primary and foreign keys, ensuring referential integrity. Relationships are labeled with verb phrases such as 'receives,' 'violates,' and 'is issued to' to clarify their semantic meaning.

The process underscores the importance of thorough dependency analysis to achieve a clean, normalized schema that minimizes redundancy, enhances integrity, and promotes efficient data management. Throughout this process, challenges such as identifying transitive dependencies and correctly partitioning relations were addressed by iterative analysis and consultation of normalization principles.

Reflections on the Normalization Process

Throughout the completion of tasks 1 and 2, several challenges emerged, notably in correctly identifying the functional dependencies amidst complex attribute relationships and ensuring the decomposition preserved data integrity without over-normalization. Initially, understanding the dependencies required careful analysis of attribute roles and their interactions, which sometimes involved making assumptions based on domain knowledge. This was resolved by cross-validating dependencies through multiple examples and diagrams.

The decomposition process highlighted the necessity of systematic approach—breaking relations into smaller, manageable segments aligned with normal forms—while maintaining referential integrity. The use of ER modeling tools like ERwin facilitated visualization, ensuring that all primary and foreign keys were properly linked and constraints set.

One key principle learned is that normalization, beyond achieving theoretical correctness, necessitates balancing design efficiency against application-specific requirements. Over-normalization might lead to complex queries, so pragmatic decisions must be made to optimize performance without sacrificing data quality. Additionally, explicitly defining relationships using meaningful verb phrases enhances the semantic clarity of database schemas, making them easier to understand and maintain.

In conclusion, practicing normalization fostered a deeper understanding of data dependencies, schema design, and integrity constraints. These principles are applicable across various contexts, highlighting the importance of thorough dependency analysis, systematic decomposition, and clear diagramming in effective database management.

References