THE DATA WAREHOUSE - Fundamentals of Database Management Systems - page 351

contain aggregated data. Every car rental transaction is recorded for posterity in the
data warehouse. Notice that this data warehouse has a snowflake feature since the
CAR dimension table is connected outwards to the MANUFACTURER table. The
query possibilities in this data warehouse are very rich. Lucky could ask how many
mid-size (the CAR table's Class attribute) General Motors cars were rented on July
weekends in each of the last five years. To find who some of their most valuable
customers are for marketing purposes, Lucky could identify the customers (and
create a name and address list for them) who rented full-size cars at least three times
for at least a week each time during the winter months of each of the last three years.
Or, using the Manufacturer Country attribute of the MANUFACTURER table in the
snowflake, they could find the amount of revenue (based on the RENTAL table's
Cost attribute) that they generated by renting Japanese cars during the summer
vacation period in each of the last eight years.

What About a World Music Association Data Warehouse?
Did you notice that we haven't talked about a data warehouse for the World Music
Association (WMA), whose transactional database is shown in Figure 5.17? If there
were to be such a data warehouse, its most likely subject would be RECORDING,
as the essence of WMA's business is to keep track of different recordings made of
different compositions by various orchestras. There is already a Year attribute in
the RECORDING table of Figure 5.17. In this sense, the main data of the World
Music Association's transactional database is already ''timestamped,'' just like
Good Reading Bookstores' and Lucky Rent-A-Car's data. We gave reasons for
creating data warehouses for Good Reading and for Lucky, so what about WMA?
First, the essence of the WMA data is historical. We might be just as interested
in a recording made fifty years ago as one made last year. Second, by its nature,
the amount of data in a WMA-type transactional database is much smaller than the
amount of data in a Good Reading or Lucky-type transactional database. The latter
two transactional databases contain daily sales records in high-volume businesses.
Even on a worldwide basis, the number of recordings orchestras make is much
smaller in comparison. So, the conclusion is that, since the nature of the WMA
transactional database blurs with what a WMA data warehouse would look like and
the amount of (historical) data in the WMA transactional database is manageable,
there is no need for a WMA data warehouse.

13.1 D ESIGNING A U NIVERSITY D ATA W AREHOUSE

YOUR

TURN

U niversities create a great deal of
data. There is data about students, data about professors,
data about courses, data about administrative units such
as academic department, data about the physical plant,
and accounting data, just as in any business operation.
Some of the data is current, such as the students enrolled
in particular courses in the current semester. But it may be
useful to maintain some of the data on a historical basis.

Q UESTION :
Think about what data a university might want to maintain
on a historical basis. Design a data warehouse for this
historical data. You may focus on students as the subject
of the data warehouse or any other entity that you wish.