Data Warehouses and the Semantic Web - Data Warehouse Systems: Design and Implementation - page 565

Sales
product id time id customer id promotion id store id store sales store cost unit sales
219 738 567 0 1 7.16 2.4344 3

684 738 567 0 1 12.88 5.0232 2

551 739 639 7 2 5.20 2.236 4

Product
product id product name brand name product class id

219 Best Choice Corn Chips Best Choice 12

551 Fast Low Fat Chips Fast 12

684 Gorilla Blueberry Yogurt Gorilla 6

Product class
product class id product subcategory product category product department product family
6 Yogurt Dairy Dairy Food

12 Chips Snack Foods Snack Foods Food

Time by day
time id the date the day the month the year day of month week of year month of year quarter

738 1998-01-07 Wednesday January 1998 7 4 1 Q1

739 1998-01-08 Thursday January 1998 8 4 1 Q1

Customer
cust id state country marital

status

yearly
income

fname mi lname city gender education

567 Charles L. Christensen Santa Fe DF Mexico S $50K-$70K F Bachelors

639 Michael J. Troye r Kirkland WA USA M $30K-$50K M High School

Promotion
prom id prom name media type

0 No Promotion No Media

7 Fantastic Discounts Sunday paper, Radio, TV

Store
store id store type store name store city store state store country store sqft

1 Supermarket Store 1 Acapulco Guerrero Mexico 23593

2 Small Grocery Store 2 Bellingham WA USA 28206

Fig. 14.9 An instance of the Foodmart data warehouse

14.7 Represent the Foodmart cube schema using the QB4OLAP vocabulary.
14.8 Consider the Foodmart table instances given in Fig. 14.9 a. Represent
sales facts as observations, adhering to the Data Structure Definition
specified in the previous exercise.
14.9 Write in SPARQL the queries over the Foodmart cube given in Ex. 4.9 .