1. Exercises

2. Exercise (1.1)
Suppose the AAA Automobile Co. builds a data warehouse to analyze sales of its cars.
The measure - price of a car
We would like to answer the following typical queries:
find total sales by day, week, month and year
find total sales by week, month, ... for each dealer
find total sales by week, month, ... for each car model
find total sales by month for all dealers in a given city, region and state.

3. Exercise (1.2) Dimensions: Design the conceptual data warehouse schema
time (day, week, month, quarter, year)
dealer (name, city, state, region, phone)
cars (serialno, model, color, category , …)
Design the conceptual data warehouse schema

4. Exercise (2.1)
Our chain of hotels operates 500 hotels in 40 countries. In our chain, we have different types of hotels (5-stars, 4-stars, 3-stars, motels, etc.). We would like to store information about each hotel and perform analysis with regard a given hotel. For each hotel we store its name, type, address, country, region, zip code, phone, name of the manager. There are also different types of rooms like single, double, family, suits, etc. Each room may also incorporate certain optional features, such as refrigerator or kitchenette. So, each room may be described as foolws: room’s type, size, number of beds, max-number_of_customers, refrigerator (Boolean), kitchenette (Boolean).

5. Exercise (2.2)
The hotel chain’s capacity to accomodate customers is limited. Each hotel has a set number of rooms.
Our primary ssource of revenue is accomodation in hotel rooms. The biggest challenge we face iss determining how to price our hotel rooms. If they are priced low, our hotels will be constantly booked, the people will be forced to try the competition out, and we won’t any money. If rooms are priced too high, a lot of rooms will remain empty.
To determine how to price hotel rooms our analysts need a look at the use of our capacity over time: the occupancy rate (utilization) and the vacancy rate.

6. Exercise (2.3) We would like to perform the following analysis:
find total number of occupied rooms (occupaancy rate) by week, month, year for each type of a hotel and each type of a room.
find total number of vacant rooms (vacancy rate) by week, month, year for each type of a hotel and each type of a room.
rates are seasoned, so we are interested at utilization and vacancy rates at specific periods in time,
find total number of guests staying in occupied rooms by week, month, year for each type of a hotel and each type of a room.
we would like to analyze preferable locations of hotels.

7. Exercise (2.4) We would like to perform the following analysis:
Our guets prefer rooms woth additional features or they prefer standard rooms
Shall we invest in 3-stars, 4-stars, or 5-stars hotels?
Etc.
Design the conceptual data warehouse schema

8. Exercise (3.1)
We would like to design a data warehouse for a regional weather bureau. The weather bureau has about 1000 probes, which are scattered throughout various land and lake locations in the region to to collect basic weather data including air pressure, temperature, and precipitation at each hour. All data are sent to the central station, which has collected such data over 10 years.. The designn should facilitate efficient analysis and on-line query processing, and derive general weather pattterns.

9. Exercise (4.1)
We would like to design a data warehouse for Primera Division (football leaque). We would like to analyze games, the number of spectators watching games, the number of tickets sold for spectators, and revenue got from the sale of tickets. Games are organized at different locations. For each location we would like to keep track of its name, address, capacity, and some additional features like ‘closed’ or ‘open’. For each game we would like to store information about teams playing game, result of the game, etc. Spectators may be students, adults, or seniors, with each categgory having its own charge rate.

10. Exercise (4.2) We would like to answer the following typical queries:
find the total number of spectators watching games played by Real Madrid by years and by locations,
find the total number of seniors watching games organized in Madrid by years,
find the total charge that students paid for watching games in Klagenfurt by months and years,
find the total charge that spectators paid in 2001 by diffeerent locations and different categories of spectators.