Presentation on Datawarehousing in Transportation Chapter (12) in the Datawarehouse Toolkit textbook by Ralph Kimball & Margy Ross.

1. Data Warehouse
Transportation

2. Outline
1. Requirement
2. 4 Step Design
3. New Techniques
4. Improvement

3. Techniques
1. Fact tables at different levels of granularity
2. Combining correlated role-playing dimensions
3. Country-specific date dimensions
4. Dates and times in multiple time zones

4. Bottom-up

5. Airline’s Bus matrix
Business Process
Common Dimensions

6. Example airline database

8. STEP DESIGN4

9. Step 0: Requirement
The marketing department wants to analyze:
- what flights the company’s frequent flyers take
- what fare basis they pay, how often they upgrade
- how they earn and redeem their frequent flyer miles
- whether they respond to special fare promotions
- what proportion of these frequent flyers have gold, platinum, aluminum,
or titanium status

10. Step 1: Identify Business Process
*Note :
Reservation or ticketing activity
data that didn’t result in a
passenger boarding a plane is
not considered

11. THA JAP USA
TG 001 TG 001
Leg Leg
Segment
Step 2: Granularity

12. THA JAP USA
TG 001 TG 002
Segment Segment
Trip
Leg Leg
Step 2: Granularity

13. THA JAP USA
TG 001 TG 002
Trip
TG 004 TG 003
Itinerary
Origin Destination
Step 2: Granularity

14. ● Segment level : each flight (same flight No.)
● 1 row in the fact table for each boarding pass collected
What can be determined?
● passenger revenue and mileage credit
Focus on..
● Marketing and Revenue groups
Step 2: Granularity

15. Multiple Fact Table Granularities
Level Use cases Note
Leg (atomic)
take off to landing
- Capacity planning analysis
- Flight scheduling analysis
number of seats, flight
duration and the number
of minutes late at
departure and arrival,
identify on-time arrivals
Segment
single flight number
- passenger revenue and mileage credit
- marketing and revenue groups
lowest level of data with
meaningful revenue
metrics
Trip
One-way trip
May change flight (transfer)
- Get an accurate picture of customer
demand
- Sales and marketing
Itinerary
Whole ticket/reservation
number
- Want to have whole idea of the travel

16. Step 3:
Identify
Dimensions...
Dimensions Technique
1. ScheduleDepartureTime
2. ActualDepartureTime
Multi-Role Playing
(DateDim)
1. ScheduleDepartDate
2. ActualDepartDate
Multi-Role Playing
(TimeDim)
1. SegmentOriginAirportDim
2. SegmentDestinationAirportDim
Multi-Role Playing
(AirportDim)
1. ClassPurchase
2. ClassFlown
3. ClassChange
Combine to
ServiceClassDim
PassengerDim -
AircraftDim -
FareBasisDim -
PromotionDim -
BookingChannelDim -
Degenerated Dimension
ConfirmationNumberID
TicketNumberID
FlightNumberID
SegmentSequenceNumberID

17. Linking Segments into Trips
Segment can’t answer one of the most important questions about your
frequent flyers: “Where are actually they going?”
Impossible to know the trip starting and end points
SOLUTION : introduce two more airport role-playing dimensions
→ 1. TripOriginAirport 2. TripDestinationAirport

18. Combining correlated role-playing dimensions
1. Class of service
2. Origin and Destination
1. Data volumes are extremely small
2. Attributes depend on combined roles for context and meaning
When to combine

19. Class Of Service Dim
Business requirement: filter report based on whether an upgrade or
downgrade occurred
Class Purchased
Key
- Need to have an upgrade indicator
Row counts are so small
Class Flown
Key
Class Change
Key

20. ( Similar as JUNK DIM )
Combine into
single dimension
Class Of Service Dim

21. Origin and Destination Airport
Data volumes are more
significant
→ Separate tables
But
Business users may need
additional attributes that
depend on the combination of
→ Origin and Destination Table

22. Option
add another dimension to the fact table for the airport-pair route
descriptors
1

23. Option
Combine the origin and destination airport attributes, plus the
supplemental city-pair route attributes, into a single dimension
PairAir
portKey
Dpt.
Airport
Name
Dpt. Airport
City
... Arr.
Airport
Name
Arr.
Airport
City
... Directi
onal
Route
Name
Route
Distance
in Miles
Route
Distance
Band
Dom-Intl
Ind
Transocean
Ind
...
1 BOS Boston ... JFK New york ... BOS-J
FK
191 Less than
200 miles
Domestic Non-Oceanic ...
2 JFK New york ... BOS Boston ... JFK-B
OS
191 Less than
200 miles
Domestic Non-Oceanic ...
3 BOS Boston ... NRT Tokyo ... BOS-N
RT
6737 More than
6000
miles
Internatio
nal
Transatlantic ...
4 NRT Tokyo ... BOS Boston ... NRT-B
OS
6737 More than
6000
miles
Internatio
nal
Transatlantic ...
2

24. Normal Date Dimension
Consider business are across 3 countries
with different Calendars
● Need 3 extra duplicate attribute to
represents each specific Holiday.
● Redundant and become increasingly large
as number of Countries increases
Country-specific date dimensions
………………

25. What is the problems
● Different countries and multinational with different calendars
○ require sets of independent attribute of days/months/year in primary
dimension.
● Holidays occurs differently according to various calendars on different
countries or part of the world.

26. Country-specific date dimensions
JOIN to the main calendar dimension as an outrigger or directly to the fact table
Country-specific calendar
“outrigger”

27. Dates and times in multiple time zones
Multiple time zones = Multiple countries
1) Capture the date and time relative to local time
2) Express the time period relative to a standard time (GMT, UTC)
3) Separate date dimension and time-of-day dimension corresponding to
the local and equivalized dates

29. Localization Recap
1. All of database-centric technique fall under the general theme
of “localization”
2. DW/BI system built to serve business users in many countries
need to designed for these localization issues
3. Challenges: How airport control towers and airplane pilots
around the world deal with language incompatibilities when
communicating critical messages → Use one language, unit
measure ex. measure in feet

30. Step 3:
Identify
Dimensions...
Dimensions Technique
DateDim
1. ScheduleDepartureTime 2. ActualDepartureTime
3. GMTScheduledDepTime 4. GMTActualDepartureTime
Multi-Role Playing
(DateDim)
TimeDim
1. ScheduleDepartDate 2. ActualDepartDate
3. GMTScheduledDepDate 4. GMTActualDepartureDate
Multi-Role Playing
(TimeDim)
AirportDim
1. TripOriginAirport
2. TripDestinationAirport
Multi-Role Playing
(AirportDim)
SegmentAirportPairDim
1. SegmentOriginAirportDim
2. SegmentDestinationAirportDim
Combine to
AirportPairDim
ServiceClassDim
1. ClassPurchase
2. ClassFlown
3. ClassChange
Combine to
ServiceClassDim
PassengerDim
PassengerProfileDim
Mini Dim
CountrySpecificDateOutrigger Outrigger
AircraftDim -
FareBasisDim -
PromotionDim -
Degenerated Dimension
ConfirmationNumberID
TicketNumberID
FlightNumberID
SegmentSequenceNumberID
BookingChannelDim -

31. Step 4: Identify Measures

33. Step 5: Fill in Attributes
Date Dim (multi-role)
Passenger Dim
Passenger Profile Dim
Airport Dim (multi-role)
Aircraft Dim
Fare Basis Dim
Booking Channel Dim
Promotion Dim
Class of Service Dim
Segment Airport Pair Dim
Time Dim (multi-role)

34. Passenger mini-Dimension (SCD4)

35. Class Dimension (Combined)

36. City-Pair Route Dimension

37. Date Dimension
…
Country-Specific Date OutriggerSmartkey

38. Promotion Dimension
…

39. Reference
● The Data Warehouse Toolkit - Ralph Kimball & Margy Ross
● Oracle Airline Data Model
● Sample airline DB (all tables with columns)

40. Members
1. Kavin Sermsaksakoon
2. Teepob Harutaipree
3. Suppakit Krasettrakarn
4. Nattapol Puttasuntithum