The Objective Of This Project Is For Students To Appl 712154

The Objective Of This Project Is For Students To Apply The Database De

The objective of this project is for students to apply the database design principles to a real-life scenario. Students are required to create a comprehensive database system to track movies viewed by customers during the summer, including the development of normalized tables, ER diagrams, DBDL diagrams, and associated documentation. The project involves designing tables in at least third-normal form, creating visual representations such as ER and DBDL diagrams, and defining user roles, permissions, backup and archive procedures, and generating sample reports with supporting queries. The database must support tracking various movies, customer information, customer feedback ratings, and viewing details, enabling the generation of valuable reports for management and marketing purposes. Detailed rationale must be provided for design choices, and all components should adhere to best practices for database development and security.

Paper For Above instruction

Designing a comprehensive movie viewing database that captures detailed customer interactions, feedback, and viewing history requires applying fundamental database design principles, including normalization, entity-relationship modeling, and security considerations. This paper discusses the data modeling process, visualization tools, user access control, backup and archiving procedures, and reporting capabilities necessary to support effective management and decision-making in a real-world scenario centered around summer movie viewership.

Introduction

The primary goal of this database project is to develop a structured, efficient, and secure system that monitors customer movie preferences, viewing history, and feedback during a specific period. The scenario provided entails tracking multiple movies, customer demographics, and feedback ratings, with the ability to generate relevant reports for marketing and operational purposes. A well-structured database facilitates data integrity, security, and efficient querying, all of which are critical in managing large datasets derived from customer interactions.

Entity-Relationship (ER) Modeling and Normalization

Building the foundation of this database involves identifying entities such as Customer, Movie, Genre, Rating, View, and Feedback. Customers will have unique identifiers, with attributes like name, address, and contact details. Films will be characterized by attributes such as title, release date, genre, and rating. The View entity captures attributes like date viewed, customer ID, movie ID, and feedback rating. Relationships include customers viewing movies and providing ratings, necessitating foreign keys linking view records to customer and movie tables.

Applying normalization rules ensures minimal redundancy and efficient data organization. Tables are designed in at least third-normal form, with primary keys (e.g., CustomerID, MovieID, ViewID) ensuring each record's unique identification. For instance, the Movie table has a primary key MovieID, and attributes such as title and genre are wholly dependent on it, with no transitive dependencies present.

Visual Modeling: ER and DBDL Diagrams

An ER diagram visually represents entities, their attributes, and relationships—such as a 'Customer' entity linked via 'viewed' relationships to a 'Movie' entity, with attributes included in each entity. This visual aid helps clarify database structure and serves as a blueprint for implementation. Using tools like Word, Excel, or diagram software, the ER diagram will depict entities with their respective attributes and define cardinalities, such as one-to-many relationships between customers and views.

The DBDL (Database Design Language) diagram complements the ER diagram by providing a detailed schema with explicit keys, data types, and constraints for each table, including primary and foreign keys. Proper formatting and clear identification of keys (e.g., primary keys, foreign keys) ensure accurate translation of the ER design into physical database structures.

User Roles and Permissions

Effective access controls are crucial for maintaining data security and integrity. The system must accommodate different user roles:

  • Data Administrators: Responsible for managing base data such as customer profiles, movie details, genres, and ratings. They require full access to all tables for create, read, update, and delete (CRUD) operations.
  • Data Entry Staff: Tasked with inputting viewing records and feedback ratings, with permissions limited to data entry tables.
  • Management: Access to generate reports, view viewing statistics, and analyze trends. They should have read-only access to viewing data and reports but cannot modify underlying data.

Permissions are to be defined based on role, aligning with principles of least privilege and ensuring secure operation. Citing authoritative sources, role-based access control (RBAC) models promote data security by restricting user capabilities according to their responsibilities.

Backup and Archive Procedures

Regular backup procedures are essential to prevent data loss. A common approach involves scheduled full database backups weekly and incremental backups daily, utilizing automated scripts that create backups stored securely off-site or on cloud storage solutions. Rationale stems from minimizing downtime and ensuring disaster recovery readiness.

Archiving strategies include migrating historical viewing data to separate archival storage, freeing operational database space and improving performance. Automating these processes with clear schedules ensures data retention policies are met, compliance with legal standards maintained, and historical data preserved for auditing or analytical purposes. Technologies like backup software (e.g., Veeam, Acronis) and cloud archiving services support these objectives.

Potential Reports and Supporting Queries

The database must support generating various reports that inform management decisions and marketing strategies:

  1. Most Viewed Movies During Summer: A report listing movies with the highest view counts, which helps identify popular films for future promotions. Query would count views per movie, sorted descending.
  2. Customer Viewing Patterns: A report showing customer frequency of movie viewing, aiding in targeted marketing; grouping by customer ID.
  3. Rating Analysis by Genre: A report analyzing average ratings per genre to assess customer preferences; grouping by genre and calculating averages.

Sample supporting query for most viewed movies:

SELECT MovieTitle, COUNT(*) AS ViewCount


FROM View


JOIN Movie ON View.MovieID = Movie.MovieID


GROUP BY MovieTitle


ORDER BY ViewCount DESC;

Such queries enable quick extraction of insights, facilitating operational adjustments and strategic planning.

Conclusion

Developing a robust movie viewing database requires careful planning, normalization, clear entity-relationship modeling, and rigorous security and backup procedures. The integration of detailed tables, visual models, role-based permissions, and well-constructed reports ensures the system will be both functional and secure, supporting effective decision-making and providing valuable insights into customer preferences and viewing behaviors. Proper application of database principles, combined with supporting documentation and rationale, guarantees the success of this project in a real-world context.

References

  • Casteel, K. (2022). Database Design and Development. Wiley Publishing.

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