Assessment 1 Brief: Database Modelling KC7013 Programmes MSc
Assessment 1 Briefdatabase Modelling Kc7013programmesmsc Computer
Assessment of the 'Academic Information System' scenario requires designing a comprehensive database model to support the university's academic activities. This involves creating an entity-relationship diagram or normalized relations for the data, implementing this design in SQL, populating the database with sample data, executing retrieval queries, and analyzing professional, legal, ethical, and security considerations. Additionally, a comparison of database design approaches and justifications for the chosen method are required, supported by proper Harvard-style references.
Paper For Above instruction
The development of an effective and reliable academic information system (AIS) for the University of Gharnata necessitates a robust database design that captures all relevant data entities, their attributes, and relationships. This process begins with conceptual modeling, proceeds with logical normalization, and culminates with physical implementation in SQL, ensuring data integrity, security, and compliance with legal and ethical standards. This paper discusses the steps taken to model the AIS, including creation of an entity-relationship diagram, transformation into normalized relations, implementation using SQL, and subsequent data population and querying. It additionally examines professional, legal, ethical, and security issues associated with managing university data and compares various database design methodologies, justifying the approach adopted.
Database Design Approach and Rationale
For designing the AIS for the University of Gharnata, a hybrid approach utilizing Entity-Relationship (ER) modeling followed by normalization was chosen. ER modeling offers a visual, intuitive understanding of the data structure, making it accessible to stakeholders for verification before technical translation. The ER diagram captures core entities such as Students, Faculty Members, Departments, Courses, Modules, and their relationships including Enrollments, TaughtModules, and DepartmentChairship. The diagram incorporates key attributes and identifies primary keys, ensuring a clear depiction of data dependencies without including foreign keys, which are assigned during the mapping process.
Post ER modeling, the schema was mapped into relations, with care taken to normalize the relations to the Third Normal Form (3NF). Normalization reduces data redundancy, enhances consistency, and simplifies maintenance. For example, Student personal details are stored in a separate Student table, with address and next-of-kin contacts broken down into related tables. This systematic structuring ensures that the database adheres to best practices for data integrity and efficiency. The dual use of ER modeling and normalization combines the strengths of conceptual clarity with rigorous data structuring, making it suitable for a complex university environment.
Implementation in SQL
The relations derived from the ER diagram underwent implementation using Oracle SQL. The data definition language (DDL) statements, including CREATE TABLE, specify attributes with appropriate data types, and include constraints such as PRIMARY KEY, FOREIGN KEY, CHECK, and NOT NULL to enforce data validation and integrity. For example, the Faculty table includes constraints on email format, salary range, and date of joining, ensuring compliance with data standards and security policies. Sample data was inserted using INSERT statements to facilitate testing and demonstration of queries.
Sample output from executing the SQL scripts verified the successful creation and population of the database. For instance, queries retrieving student details, course enrollments, and faculty information showcased the database's utility. Relational features like cascading delete and update constraints were used with care to prevent data anomalies, aligning with ACID properties vital for academic recordkeeping. The use of explicit constraints and indexes optimized query performance and data security.
Data Population and Retrieval
Populating the database involved generating sample records for students, faculty members, departments, courses, and modules representative of university data. This included nested data such as student addresses and next-of-kin, faculty qualifications, and module details. The INSERT scripts were designed to preserve referential integrity, with foreign keys referencing existing primary keys.
Key SQL and relational algebra queries were crafted to extract meaningful information. For example, a query displayed the names of postgraduate students with their enrolled modules and marks, illustrating how the database supports academic assessment. Another query listed all personnel involved in the AIS, including students and staff, along with their addresses and departmental associations, demonstrating data retrieval capabilities.
Professional, Legal, Ethical, and Security Considerations
Implementing an AIS at a university involves confronting numerous professional, legal, ethical, and security issues. Data privacy is paramount; student and staff personal data must be protected in accordance with data protection laws such as GDPR, which necessitate secure storage, restricted access, and audit trails. Ethical principles dictate that data is used solely for authorized purposes, avoiding misuse or unauthorized sharing.
Legal compliance also requires transparent data handling policies and obtaining consent from individuals whose data is stored. Professionally, database designers must ensure system availability, reliability, and maintainability, supporting the university's academic mission. Security measures such as encryption, strong authentication, and regular audits safeguard sensitive data from breaches and cyberattacks. Furthermore, ethical concerns include ensuring equitable access to data and preventing discriminatory use of information.
Comparison of Database Design Approaches
Different methodologies for database design offer distinct advantages and limitations. Entity-Relationship modeling provides a high-level, intuitive view suitable for initial stages and stakeholder communication, but requires further normalization to optimize the schema. Normalization alone ensures efficient, redundancy-free data storage but can struggle to convey complex relationships visually. Conversely, object-oriented database design supports complex data types and inheritance, but may lack standardization and widespread adoption in academic contexts.
The chosen approach—combining ER modeling with normalization—strikes a balance between conceptual clarity and data integrity. This method facilitates clear visualization of complex relationships like courses, modules, and personnel, while ensuring the underlying relations are optimized for storage efficiency and data consistency. The approach is justified for this scenario, given the interconnected nature of university data and the need for flexible yet reliable data management.
Conclusion
The design and implementation of the AIS for the University of Gharnata highlight the importance of a disciplined, standards-compliant approach to database development. The amalgamation of ER modeling and normalization provides a solid foundation for a maintainable, secure, and ethically managed system. Addressing professional and legal issues ensures compliance and trustworthiness, while rigorous security protocols protect sensitive data. The methodology adopted in this project demonstrates best practices for academic database design, serving as a model for similar institutional systems. Continuous review and updates, aligned with evolving legal and technological standards, are essential to sustain the system’s effectiveness and integrity.
References
- Elmasri, R., & Navathe, S. B. (2015). Fundamentals of Database Systems (7th ed.). Pearson.
- Coronel, C., & Morris, S. (2015). Database Systems: Design, Implementation, & Management. Cengage Learning.
- Northumbria University. (2018). Quick guide to Referencing and Plagiarism. Retrieved from https://www.northumbria.ac.uk
- Pears, R., & Shields, G. (2008). Cite them right: the essential referencing guide. Pear Tree Books.
- Date, C. J. (2004). An introduction to database systems. Addison-Wesley.
- Harrington, J. L. (2016). Relational Database Design and Implementation. Morgan Kaufmann.
- Teorey, T. J., & Lightstone, S. (2011). Database Modeling & Design. Morgan Kaufmann.
- ISO/IEC 27001. (2013). Information security management systems—Requirements. International Organization for Standardization.
- European Union. (2016). General Data Protection Regulation (GDPR). Regulation (EU) 2016/679.