Data Modeling Using CityTrain Transit System

Data Modeling using Draw.io: CityTrain Transit System (CTTransit) 

Using Draw.io and the attached “CTTransit_StartingPoint” diagram as your starting point, create an Entity Relationship Diagram (ERD) of the CTTransit business, as described in the case above. Specify all Entities, Relationships, and Attributes, including primary keys, volume estimates, data types, and descriptive labels. Ensure all relevant facts from the case are incorporated, with foreign keys italicized. Relationship lines must not cross or overlay. Only these ten entities are required; do not add or assume new ones. The final diagram should fit on one 8.5" x 11" page when printed. Work must be done individually, with no copying or modification of others’ work. The process involves producing an interim model focused on entities, relationships, and primary keys, then refining it by adding attributes from the case, applying normalization rules. The completed data model must correctly depict entities, relationships, and attributes, with proper naming, data types, and key notation. The deliverable is an electronic file created in Draw.io, named correctly, and submitted via eClass on time.

Paper For Above instruction

The CityTrain Transit System (CTTransit) presented in the case study is a complex urban transportation network requiring a detailed and accurate entity-relationship model to facilitate effective database design. The primary objective is to develop a comprehensive data model that encompasses entities, relationships, and attributes, capturing the system's essential components and their interactions to support operational efficiency and data integrity.

In constructing the ER diagram, the first step involves identifying key entities based on the case description. These entities include Pass, Point of Sale (POS), Station, Turnstile, Platform, Line, Trip, Fare, Employee, and Pass Type. Each of these entities plays a vital role in the system's functionality. For example, the Pass entity tracks individual passes issued to passengers, including relevant attributes such as pass number (primary key), purchase date, expiry date, balance, and pass type foreign key referencing Pass Type. Similarly, the Station entity comprises station code (primary key), name, location description, area, and number of elevators, serving as a basis for other related entities like Platform and Turnstile.

Relationships among these entities are established based on their real-world interactions. Passes are associated with Trips, which are logged when passengers use turnstiles at stations. A Trip is characterized by a start and end turnstile, capture date and time, and fare details. The relationship between Pass and Trip is one-to-many, as each pass can record multiple trips, up to certain archive limits. The entity relating to the Point of Sale includes attributes such as POS ID, location, self-operation indicator, and last replenishment date, with relationships to Passes and employees who perform the sales. Lines connect to platforms, with each platform associated with a specific station and line to represent train routes.

Attributes are meticulously assigned to their respective entities, ensuring normalization and data integrity. Primary keys are underlined in the diagram, and foreign keys are italicized. For example, the Fare entity includes fare ID (primary key), effective date, start station code, end station code, regular fare amount, reduced fare amount, and expiry date. Foreign keys such as start station code and end station code link to the Station entity, identifying the stations involved in fare transactions. The model also estimates the volumes of data, such as the creation of 500,000 passes in the first month, with subsequent replacements, and an average daily trip count of 300,000, considering data retention limits of three years.

Finally, to ensure clarity and usability, relationship lines must be drawn so that they do not cross or overlay, making the diagram easily interpretable. The ER model serves as a cornerstone for implementing the database that will support CTTransit's operations, including pass management, fare calculation, trip logging, and system administration. By adhering to normalization principles and accurately representing the system's data requirements, the ER diagram will facilitate the development of a robust and scalable database system capable of managing the complex interactions within CTTransit.

References

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