This system basically refers to the reservation and cancellation of train tickets for the passenger. The need for this system is because, as is well known, India has the largest railway network in the world and it is not possible to handle such a large system manually. Through computerization, it became possible to overcome the limitations and make the operations of the system more efficient. The complexity in handling data and records of such a vast system was reduced and made easier by computerizing the system. Being more specific, this online railway reservation system can perform the basic functions like reservation and cancellation. Users are required to register to the server to gain access to the database and retrieve query results. At the end of registration, each user has an account that essentially refers to the customer's 'view level'. The account contains complete information of the user entered during the registration and allows the user to access their past reservations, cancellations, find out about trains and train schedules, seat availability and make reservations again. The main user can also update their account details, etc. The master user of this system is the railway administrator who can log in with a master password and once a user is authenticated as an administrator, he can access and modify the information stored in the database of this system.
Star schema
In computing, the star schema is the simplest style of the data mart schema and is the most widely used method for developing data warehouses and data data. The star schema consists of one or more fact tables that refer to any number of dimension tables. The star schema is an important special case of the snowflake scheme, and is more effective in handling simpler queries. The stellar scheme derives its name from the resemblance of the physical model to a star shape with a table of facts in its center and the surrounding dimensional tables that represent the points of the star.
The star schema separates business process data into facts that contain measurable quantitative data about a business and dimensions that are descriptive attributes related to factual data. Examples of facts facts include the sales price, the sales quantity and the measurements of time, distance, speed and weight. Examples of related dimension attributes include product models, product colors, product sizes, geographic locations, and vendor names.