CASE STUDY - RAIL BHARAT DIMENSIONAL MODELING – BOOKING CANCELLATION AND TRAIN OPERATION PRESENTED BY TBI32 – P2G2

Team Members Vittal H - (1004639) Gowthami K - (965879) Prashant B G - (1004628) Priya J L - (965882) Ritu - (986901)

Identifying subject area and grain of the dimension Identifying dimensional attributes Choose facts and connect fact to dimensional tables by means of surrogate keys Dimensional model KPI Report Contents

Scenario : Booking cancellation and Train operations The system provides a facility to cancel the reserved tickets for all the trains. The refund amount will vary according to the class booked, the difference between date of travel and the date of cancellation and the distance. The OLTP model captures the train , difference between the cancellation date and booking date and the refund amount. The refund amount is calculated as follows: • 30 % of ticket charge if Date of cancellation is same as Date of Travel • 45 % of ticket charge if difference between Date of cancellation and Date Of Travel is more than 1 day and less than or equal to 2 days. • 50% of ticket charge if difference between Date Of Cancellation and Date Of Travel is more than 2 days and less than or equal to 4 days . • 65 % of ticket charge if difference is more than 4

Identifying Subject Area and Grain of the Dimension Subject Area: Cancellation And Train Operation Grain: Cancellation per date per Zone

Identifying Dimensions From The Grain Primary dimensions: Rb Passenger Reservation Dimension Rb Zone Dimension Date Dimension Secondary Dimension: Rb Train Operations Dimension Rb Train Type Dimension

Identify the grain of the dimension Click to add text Primary dimensions Date Dimension – Date level Rb Zone Dimension – Zone level Secondary Dimension Rb Train Type Dimension – Train Type level

Identifying Dimensional attributes Date Dimension Date Key Integer Surrogate Key Day Day part of the Date Month Month part of the Date Quarter Quarter part of the Date Year Year part of the Date

Rb Passenger Reservation Dimension Reservation Key Integer surrogate key for the dimension Travel Id Train No Date Of Travel From Station Code To Station Code Distance Class code Number Of Passengers Amount Train Class Description

Rb Zone Dimension Zone key Integer surrogate key for the dimension Station Code Station Name Address State Zone Code Zone Name Headquarters Date Of Estd

Rb Train Operations Dimension Train Number Train Name Originating Station Destination Station Departure Time Arrival Time Distance In Kms Return Train Number Of Halts

Choose facts Fact → Number of Cancellations And Refund Amount

Connect fact to dimensional tables by means of surrogate keys Fact table Cancellation Id Primary Key Reservation Key Foreign Key pointing to the Reservation dimension Zone key Foreign Key pointing to the Zone dimension Train Number Foreign Key pointing to the Train dimension Date Of Cancellation Foreign Key pointing to the Date dimension Number Of Seats Fact Refund Amount Fact

Rb Passenger Reservation Dimension Reservation Key Travel Id Train No Date Of Travel From Station Code To Station Code Distance Class code Number Of Passengers Amount Train Class Description STAR SCHEMA Rb Train Operations Dimension Train Number Train Name Originating Station Destination Station Departure Time Arrival Time Distance In Kms Return Train Number Of Halts Cancellation Fact Cancellation Id Reservation Key Zone key Train Number Date Of Cancellation Number Of Seats Refund Amount Rb Zone Dimension Zone key Station Code Station Name Address State Zone Code Zone Name Headquarters Date Of Estd Date Dimension Date key Day Month Quarter Year

KPI- Key Performance Indicators Number of cancellation per train type Number of cancellation per class type Revenue generated in each zone

Report 1 – Sample data for No_of_Cancellations per Train Type 50 exp 80 drnt 20 skr 36 hyd 79 klkt 12 pass 69 memu 99 del 44 raj

Report 2 – Sample data for No_of_Cancellations per Train Type 60 exp 26 drnt 54 skr 15 hyd 52 klkt 42 pass 25 memu del 75 raj

Report 3 – Sample data for No_of_Cancellations per Train Type 14 exp 86 drnt 45 skr 36 hyd 25 klkt 35 pass 95 memu 13 del 82 raj

Report 1– Sample data for No_of_Cancellations per Class Type 15 SL 80 3A 35 2A 45 1A 12 CC 44 2S 69 Ex 29 Fc

Report 2– Sample data for No_of_Cancellations per Class Type 35 SL 41 3A 15 2A 43 1A 65 CC 14 2S 25 Ex 68 Fc

Report 3– Sample data for No_of_Cancellations per Class Type 51 SL 85 3A 38 2A 46 1A 27 CC 82 2S 34 Ex 12 Fc

Report – Sample data for Revenue generated in each zone Revenue (lakhs) Zone 10 NR 50 SR 16 CR 67 WR 80 NFR 76 NER 29 SWR

Report – Sample data for Revenue generated in each zone Revenue (lakhs) Zone 25 NR 15 SR 68 CR 29 WR 38 NFR 42 NER 23 SWR

Report – Sample data for Revenue generated in each zone Revenue (lakhs) Zone 24 NR 56 SR 34 CR 75 WR 46 NFR 14 NER 86 SWR

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