Dimension table names
Fact Table, Granularity and Justification against Granularity:
|
Fact table name |
Fact granularity |
Brief justification |
|
Rental and Sales Fact Tale |
TotalRevenue |
The organization is running the business to make profit and grow business. The growth of the business is reflected through the revenue. For this reason, it is very crucial to collect the information of revenue. |
|
CarRentAtPromotion |
This fact shows the business growth at processing level |
|
|
ItemSellQuantity |
The organization also make profit from selling the items |
|
|
RevenueInNormalPeriod |
Most of the time business runs on normal period |
|
|
RevenueAtPromotionPeriod |
Business can find the quantity of rented car so that proper business decision can be made |
|
|
ADVERTISEMENT_COMMERCIAL |
PromotionCost |
It is essential for the organization to reduce cost of promotion so that profit can be increased |
|
PromotionSuccess |
The total amount of capital collected form a promotion |
|
|
RentAmount |
The amount of car rented because of a promotion |
|
|
MediaCompanyProfit |
The amount of additional capital CarhireOZ makes from a media promotion |
|
|
ProtionDuration |
The duration of the promotion. This assists in understanding the impact of promotion and reduce business by adjusting promotion duration next time |
4. Justification and Attribute Hierarchy of the Dimension Tables:
|
Dimension table name |
Brief justification |
Attribute hierarchies |
|
BOOKING_DIMENSION |
The booking allows the organization to determine whether full online business is ready to be accepted or not |
|
|
CONTRACTS_DIMENSION |
Contacts act as the proof of settled deals. This dimension allows the organization to collect additional processes when customer violates a rule. Based on these data, the organization can collect payment legally |
|
|
CUSTOMER_DIMENSION |
Customers are the base of business. Having required data of the customer allows to expand business and better promotion option |
|
|
Date_Dimension |
The date dimension allows the system to have full information of the values in timeline aspect |
|
|
DRIVER_DIMENSION |
Having the details of the drivers allows the organization to manage the business legally and manage processes |
|
|
Item_DIMENSION |
The dimension table allows the organization to have item related data |
|
|
Media_Company_DIMENSION |
This dimension allows the organization to find the details of the media companies |
|
|
Member_Class_DIMENSION |
CarhireOZ will be using these data to identify the customer expense capacity. The promotions will be made as per the customer capabilities |
|
|
Product_Promotion_DIMENSION |
The promotion of the product ensures that whether the product sale will increase or not. |
|
|
RETURN_PROTOCOL_DIMENSION |
The return details are very crucial so that business operations can be operated smoothly. |
|
|
STORES_DIMENSION |
The sales from each store is significant data. CarhireOZ will make various decisions based on sales made from stores. |
|
|
VEHICLE_DIMENSION |
CarhireOZ must keep all the information of the cars they own. This allows the organization to have better insight of business allow further growth. |
|
|
Vehicle_type_DIMENSION |
The organization needs to have the information on the types of vehicle they own |
5. Data Warehouse Design Features:
|
Design feature |
Brief description |
Brief justification |
|
Fact less fact tables |
The fact less fact table is the type of fact table that do not have any fact or measure |
In the selected data warehouse consent, the fact tables have proper and significant facts or measures |
|
Degenerate dimensions |
The degenerated dimension in the fact table is referred to the key which has no dimension associated with it |
The sales order number can be referred to as the Degenerate dimension in the proposed Rental and Sales Fact Tale |
|
Role playing dimensions |
Role playing dimension is often referred to as the dimension used in the same database repeatedly for various reasons |
The DATE_DIMENSION is the ideal role playing dimension in the proposed data warehouse design |
|
Bridge Table |
The bridge table is used for diving the many to many implementing the database physically |
There is no bridge table in the data warehouse |
|
Junk Dimension |
The junk dimension stores the answer of a query in yes no only. No other query can be resolved through this dimension |
The bookings_dimension is the junk dimension. It only refers to answer, whether the booking has been done online or not. |
6. Identification of Fields:
Question 1:
Advertisement_Commercials: PromotionKey, registrationKey, DateKey, PromotionCost, PromotionSuccess
Date_Dimension: Day_name, Month_Name
VEHICLE_DIMENSION: VehicleModel
Question 2:
Advertisement_Commercials: PromotionKey, CompanyKey, registrationKey, DateKey, PromotionCost, PromotionSuccess
Date_Dimension: Day_name, Month_Name
Media_Company_DIMENSION: CompanyName, CompanyType
Question 3:
RENTAL_SALES: registrationKey, DateKey, customerKey, bookingNO, CarRentAtPromotion, RevenueAtPromotionPeriod
DATE_DIMENSION: DAY_NAME, MONTH_NAME
BOOKING_DIMENSION: BookingType
CUSTOMER_DIMENSION: CustomerLastName, CustomerFirstName
Question 4:
RENTAL_SALES: registrationKey, DateKey, bookingNO, CarRentAtPromotion, RevenueAtPromotionPeriod,
Date_Dimension: DATE, Month
BOOKING_DIMENSION: BOOKINGSNO
Question 5:
RENTAL_SALES: registrationKey, DateKey, customerKey, bookingNO, contractKey, StoreKey
STORES_DIMENSION: StoreName, StoreAddress
Date_Dimension: Year
CONTRACTS_DIMENSION: Rent_Start_Date
CUSTOMER_DIMENSION CustomerLastName, CustomerFirstName
Question 6:
RENTAL_SALES: registrationKey, BookingNO, returnprotocolKey, CarRentAtPromotion, RevenueInNormalPeriod, RevenueAtPromotionPeriod
Date_Dimension: Year
VEHICLE_DIMENSION: VehicleModel, VehiclePicture
BOOKING_DIMENSION: BookingType
Question 7:
RENTAL_SALES: customerKey, bookingNO, contractKey
BOOKING_DIMENSION: BookingType
CONTRACTS_DIMENSION: Rent_Start_Date
CUSTOMER_DIMENSION: CustomerLastName, CustomerFirstName
Question 8:
RENTAL_SALES: customerKey, contractKey
CONTRACTS_DIMENSION: Contact_paymentmethod
CUSTOMER_DIMENSION: CustomerLastName, CustomerFirstName
Azez, H. S. A., Khafagy, M. H., & Omara, F. A. (2015). Joum: An indexing methodology for improving join in hive star schema. International Journal of Scientific & Engineering Research, 6, 111-119.
Chowdhury, R., Chatterjee, P., Datta, S., & De, M. (2016, April). Pseudo mesh schema based data warehouse architecture employing encryption request algorithm and intelligent sensor algorithm for secured transmission and performance enhancement. In Foundations and Frontiers in Computer, Communication and Electrical Engineering: Proceedings of the 3rd International Conference C2E2, Mankundu, West Bengal, India, 15th-16th January, 2016. (p. 467). CRC Press.
Chowdhury, R., Chatterjee, P., Mitra, P., & Roy, O. (2014). Design and implementation of security mechanism for data warehouse performance enhancement using two tier user authentication techniques. International Journal of Innovative Research in Science, Engineering and Technology, 3(6), 165-172.
Chowdhury, R., Roy, O., Datta, S., & Dasgupta, S. (2018). Virtual Data Warehouse Model Employing Crypto–Math Modus Operandi and Intelligent Sensor Algorithm for Cosseted Transference and Output Augmentation. In Knowledge Computing and Its Applications (pp. 111-129). Springer, Singapore.
Dehdouh, K., Boussaid, O., & Bentayeb, F. (2014, September). Columnar nosql star schema benchmark. In International Conference on Model and Data Engineering (pp. 281-288). Springer, Cham.
Di Tria, F., Lefons, E., & Tangorra, F. (2014). Big data warehouse automatic design methodology. Big Data Management, Technologies, and Applications, 115-149.
George, J., Kumar, V., & Kumar, S. (2015). Data Warehouse Design Considerations for a Healthcare Business Intelligence System. In World Congress on Engineering.
Jukic, N., Vrbsky, S., & Nestorov, S. (2016). Database systems: Introduction to databases and data warehouses. Prospect Press.
Larbi, A., Malki, M., & Boukhalfa, K. (2018). Fuzzy Ontology-based Approach for the Requirements Query Imprecision Assessment in Data Warehouse Design Process Near Negative Fuzzy Operator. International Journal of Information Technology and Computer Science (IJITCS), 10(2), 18-32.
Raj, S. V. (2015). Applications of Business Intelligence in e-Commerce Websites-A Theoretical Overview of Data Warehouse Schema Design & Usage. The International Journal of Business & Management, 3(12), 8.
Solihin, W., Eastman, C., Lee, Y. C., & Yang, D. H. (2017). A simplified relational database schema for transformation of BIM data into a query-efficient and spatially enabled database. Automation in Construction, 84, 367-383.
Zhu, J., Potti, N., Saurabh, S., & Patel, J. M. (2017). Looking ahead makes query plans robust: Making the initial case with in-memory star schema data warehouse workloads. Proceedings of the VLDB Endowment, 10(8), 889-900.
Order an Essay Now & Get These Features For Free:
Turnitin Report
Formatting
Title Page
Citation
Outline
