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The business questions are often asked as follows:

  • What categories of Vehicles are less popular?
  • What Vehicle categories most often rent to full capacity?
  • What are the best ratios of the different Vehicle categories for sustained profit maximisation?
  • Are the customers hiring cars mostly for private use or business travel?
  • Are there certain times of the year we have more vehicles been hired?
  • What is the main location of those people (customers) renting online? Which age group hires the most vehicles?
  • How many percentage of customers make payment using cash?
  • Have those promotions in March attracted more customers? Any of those promoted cars have not been hired at all?
  • How much do we make on rentals in the last financial year? How does this compare to the past 2 years?
  • How much do we make on overdue fees in last 5 years?
  • Which categories of vehicles have made the highest profit?
  • Which of those selling items are popular with highest sales?
  • Whether the vehicles under promotion experienced an increase in rental during the promotional period?
  • Whether the vehicles under promotion showed a drop in rental just prior to or after the promotion thereby cancelling any gain
  • Whether the promotion was profitable
  • Any of those promoted cars in the period of December holiday did not get hired at all? What are their categories?
  • What sale items in store did not sell at all?

Facts and Dimensions for Rental and Sales and Advertisement_Commercials

Business processes

Dimensions

Veichles_ Types

VEICHLES_DETAILS

CUSTOMER_INFO

Member_Info_Class

INTERNET_BOOKING

DRIVER_DETAILS

CONTRACTS_DETAILS

RETURN_PROCESS

DATE_DETAILS_Table

Promotional_Items

ITEMS

Products_For_Sale

Store_Details

Media_Organization_Details

Rental and Sales

X

X

X

X

X

X

X

X

X

X

X

X

Advertisement_Commercials

X

X

X

X

X

X

X

X

The two business cases here are the Rental and Sales and Adevrtisement_Commercials. The associated facts and dimensions required for the two business processes are given in the above table. The crossed marks are presenting the facts and dimensions associated with the business processes.

The two diagrams given in the above section are representing the two business cases’ star schema. All the associated dimensions and facts of the databases are shown in the diagrams in table format. The rows of the tables are representing the attributes that will be designed in the database.

Fact table name

Fact granularity

Brief justification

RENTAL_SALES

RentsInTotal

The monetary value that will be come from the rents of the cars will be stored in this fact.  

Rents_while_Promoting

The amount of the money that will be get from the rents at the time of promotional activities, will be stored in this fact.  

Sold_Products

Details regarding the already sold products will be stored in this fact.  

Total_Earned_Revenue

Revenue earned by the company from all business activities will be stored in this fact.   

Start_Date_For_Rent

When a customer will rent a car, then the starting date of the rent will be stored in this fact.  

End_Date_For_Rent

The return dates of the rented car will be stored in this fact.

ADVERTISEMENT_COMMERCIAL

Advertisement_Cost

The cost associated with the advertisements will be stored in this fact.  

Profit_From_Adds

The profit amount got from the advertisements will be stored in this fact.  

Rents_while_Promoting

This fact is for keeping the numbers of cars given in rent at the time period of the promotional activities.

Profit_From_Media_Organization

The profits got from the advertisement companies will be stored in this fact.

Advertisements_time_Duration

This fact is for recording the time duration required for the advertisements.   

Dimension table name

Brief justification

Attribute hierarchies

VEICHLES _TYPES

In this table, the description and price of the cars will be stored with reference to a unique typeID.

typeID (PK)

description

price

VEICHLES_DETAILS

In this table, the details bout the car models with the other important specifications will be stored.  

registrationID (PK)

colour

Year_Of_Manufacturing

model

mileage

availability

photo

CUSTOMER_DETAILS

All the necessary details regarding the customers of the company will be stored in this table. Each customer has an unique ID.   

Customer_ID (PK)

Last_Name

First_Name

birthDate

address

city

state

zip

phone

MEMBERS_DETAILS

Having an unique classID, the details about the members classes will be stored in the table.  

ClassID (PK)

Classdescription

Recency

Frequency

Life_Time_Value

INTERNET_BOOKING

The customers are able to book the cars for rent over internet. This details will be stored in this table.  

BookingNO (PK)

Pre_booking

DRIVER_DETAILS

The necessary details regarding the drivers will be stored in this table. This is very important for the safety of the cars and  customers at the time of rent.

DriverID (PK)

Last_Name

First_Name

birthDate

address

city

state

zip

phone

licenceNO

creditcard

Contracts_Details

The details regarding the insurance, condition of the cars and the mileage, will be stored in this table.  

CONTRACTID (PK)

insurance

paymentmethod

condition

limitmileage

RETURN_PROCESS

When a customer will return a car after renting, the details will be stored in this table.

RETURN_PROCESSID (PK)

damage

usage

overduedays

totalcost

DATE_DETAILS

All the time scales regarding the rent, purchase, sale and promotions will be stored in this table.   

Date_ID (PK)

Date

Day

DayOfWeek

Month

Year

Quarter

PROMOTION

The types of the advertisements and money required for those will be stored in this table.

PromotionID (PK)

Type

Cost

ITEMS

All the products of the company with the details like type, name and costs will be stored in this table.  

ITEMSID (PK)

Type

Name

Cost

Products_For_Sale

The products that are ready for sale will be stored in this table.

ItemID (PK)(FK)

Customer_DetailsID (PK)(FK)

ITEMSName

Customer_DetailsName

STORE_DETAILS

Different stores are available for the organization. The details of all the stores with a unique id will be stored in this table.   

STORE_DETAILSID (PK)

Name

Address

ContactNo

FaxNo

MEDIA_ORGANIZATION_DETAILS

The details about the advertisement companies will be stored in the table.  

CompanyID (PK)

Type

Charge

Design feature

Brief description

Brief justification

Bridge Table

When many to many relation is not sufficient to represent  the schema, then the bridge table is used. This is applied in this case.

Products_For_Sale dimension table acts as the connector between Items and Sales dimensions. The primary key of each of the two tables are the foreign key in Products_For_Sale table. 

Conformed dimension

Using this relationship same queries and outcomes will be seen through different perspectives.

Customer_Details, Store_Details and RETURN_PROCESS can produce similar result from different types of queries.

Junk Dimension

This dimension will help to get the results  in just positive or negative aspects.

The Products_For_Sale is considered as junk dimension as the database will hold the information regarding the product even after sold out.

Mini Dimension

Very small changes will be stored in this dimension.  

Internet_booking is an example of this case. Whatever the status of the booking, the database will be updated.

Question 1: Whether the promotion was profitable? That is, whether the Veichles_Detailss under promotion experienced an increase in rental during the promotional period?

  • Advertisement_Commercials: PromotionID, Total_Cost_of_Promotion, Total_Profit_from_Promotion, registrationID, DATE_ID
  • DATE_DETAILS: Day, Month
  • VEICHLES_DETAILS: model

Question 2: Which media type is most effective? That is, the profitable promotion used which media type?

  • Advertisement_Commercials: PromotionID, Total_Cost_of_Promotion, Total_Profit_from_Promotion, registrationID, DATE_ID, CompanyID
  • DATE_DETAILS: Day, Month
  • MEDIA_ORGANIZATION_DETAILS: CompanyID

Question 3: Any stores have rented out more Veichles_Detailss during the promotion on Christmas holiday?

  • RENTAL_SALES:Total_Rent_Amount, Total_Rent_of_Vehilce_During_Promotion
  • DATE_DETAILS: DATE, Month
  • INTERNET_BOOKING: BOOKINGSNO

Question 4: What Veichles_Detailss were on promotion but did not rent?

  • RENTAL_SALES: Total_Rent_of_Vehilce_During_Promotion, Start_Date_For_Rent
  • DATE_DETAILS: DATE, Month
  • INTERNET_BOOKING: BOOKINGSNO

Question 5: Who are top 5 customers make a year out of all stores.

  • RENTAL_SALES: Customer_DetailsID, STORE_DETAILSID, RegistrationID, CONTRACTSID
  • DATE: Year
  • CUSTOMER_DETAILS: Last_Name, First_Name

Question 6: Which categories of Veichles_Detailss have made the highest profit in past year?

  • RENTAL_SALES: registrationID, returnprotocolid, Total_Rent_Amount
  • DATE_DETAILS: Year
  • VEICHLES_DETAILS: model
  • INTERNET_BOOKING: BOOKINGSNO

Question 7: What is the main age group of those customers renting online?

  • RENTAL_SALES: Customer_ID, BOOKINGNO
  • INTERNET_BOOKING: BOOKINGSNO

Question 8: What is the preferred payment method of customers?

  • RENTAL_SALES: Customer_Details_Key, CONTRACTID
  • CONTRACTS_DETAILS: paymentmethod

References:

Abai, N.H.Z., Yahaya, J.H. and Deraman, A., 2013. User requirement analysis in data warehouse design: a review. Procedia Technology, 11, pp.801-806.

Chaplot, V., 2016. DESIGN & IMPLEMENTATION OF DATA WAREHOUSE PROTOTYPE WITH IN THE CONTEXT OF RELATIONAL ONLINE ANALYTICAL PROCESSING (DATA ANALYSIS). International Education and Research Journal, 2(9).

Cravero Leal, A., Mazón, J.N. and Trujillo, J., 2013. A business-oriented approach to data warehouse development. Ingeniería e Investigación, 33(1), pp.59-65.

Dedic, N. and Stanier, C., 2016. An evaluation of the challenges of multilingualism in data warehouse development.

Destercke, S., Buche, P. and Charnomordic, B., 2013. Evaluating data reliability: an evidential answer with application to a web-enabled data warehouse. IEEE Transactions on Knowledge and Data Engineering, 25(1), pp.92-105.

García-Moya, L., Kudama, S., Aramburu, M.J. and Berlanga, R., 2013. Storing and analysing voice of the market data in the corporate data warehouse. Information Systems Frontiers, 15(3), pp.331-349.

Khouri, S., Bellatreche, L., Jean, S. and Ait-Ameur, Y., 2014, October. Requirements driven data warehouse design: We can go further. In International Symposium On Leveraging Applications of Formal Methods, Verification and Validation(pp. 588-603). Springer, Berlin, Heidelberg.

Kraus, C. and Valverde, R., 2014. A data warehouse design for the detection of fraud in the supply chain by using the benford’s law. American Journal of Applied Sciences, 11(9), pp.1507-1518.

Park, T. and Kim, H., 2013. A data warehouse-based decision support system for sewer infrastructure management. Automation in Construction, 30, pp.37-49.

Ross, T.R., Ng, D., Brown, J.S., Pardee, R., Hornbrook, M.C., Hart, G. and Steiner, J.F., 2014. The HMO Research Network Virtual Data Warehouse: a public data model to support collaboration. Egems, 2(1).

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