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

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

3. 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

4. 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

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

6. 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

7. 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

8. 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

9. 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

10. 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

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

12. Choose facts
Fact → Number of Cancellations And Refund Amount

13. 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

14. 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

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

16. Report 1 – Sample data for No_of_Cancellations per Train Type50
exp 80
drnt 20
skr 36
hyd 79
klkt 12
pass 69
memu 99
del 44
raj

17. Report 2 – Sample data for No_of_Cancellations per Train Type60
exp 26
drnt 54
skr 15
hyd 52
klkt 42
pass 25
memu
del 75
raj

18. Report 3 – Sample data for No_of_Cancellations per Train Type14
exp 86
drnt 45
skr 36
hyd 25
klkt 35
pass 95
memu 13
del 82
raj

19. Report 1– Sample data for No_of_Cancellations per Class Type15 SL 80 3A 35 2A 45 1A 12 CC 44 2S 69 Ex 29 Fc

20. Report 2– Sample data for No_of_Cancellations per Class Type35 SL 41 3A 15 2A 43 1A 65 CC 14 2S 25 Ex 68 Fc

21. Report 3– Sample data for No_of_Cancellations per Class Type51 SL 85 3A 38 2A 46 1A 27 CC 82 2S 34 Ex 12 Fc

22. 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

23. 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

24. 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

25. THANK YOU