Objectives: • You will continue with evaluating simple report queries and produce the output. As with
the assignment #1, you will also express the queries in SQL. The reports below are
similar in nature with the reports from the assignment #1; however, there are two main
differences between the two: (1) the new reports will require aggregation “outside” the
groups (in assignment #1, all of the aggregates were computed for the rows within the
groups); (2) some of the aggregates in the new reports will be computed based on other
aggregates of the same reports – they are known as “dependent aggregates”.
Description: • Generate reports based on the following queries:
- For each product and month, count the number of sales transactions that were
between the previous and the following month’s average sales quantities. For
January and December, display <NULL> or 0.
- For customer and product, show the average sales before, during and after each
month (e.g., for February, show average sales of January and March. For “before”
January and “after” December, display <NULL>. The “YEAR” attribute is not
considered for this query – for example, both January of 2007 and January of 2008
are considered January regardless of the year.
- For each customer, product and state combination, compute (1) the product’s average
sale of this customer for the state (i.e., the simple AVG for the group-by attributes –
this is the easy part), (2) the average sale of the product and the state but for all of the
other customers and (3) the customer’s average sale for the given state, but for all of
the other products.
- For customer and product, find the month by which time, 1/3 of the sales quantities
have been purchased. Again, for this query, the “YEAR” attribute is not considered.
Another way to view this problem (as in problem #2 above) is to pretend all 500 rows
of sales data are from the same year.
The following are sample report output (NOTE: the numbers shown below are not the actual
aggregate values. You can write simple SQL queries to find the actual aggregate values).
Yfuonctions other thanu are only allowed to the 5 aggregate functions (sum, count, avg, max & min); standard SQL syntax covered in class – do not use any other and use only
simple syntax of ‘agg(x)’ – i.e., do not use features such as CASE statement inside (such
features hide implicit JOINs).
Report #1:
PRODUCT MONTH SALES_COUNT_BETWEEN_AVGS
======= ===== ========================
Cookies 1 <NULL>
Yogurt 3 19
. . . .
CS 561 Page 1 of 2
Database Management Systems I
Spring 2020
Report #2:
CUSTOMER PRODUCT MONTH BEFORE_AVG DURING_AVG AFTER_AVG
======== ======= ===== ========== ========== =========
Bloom Coke 1 <NULL> 1539 2434 Sam Eggs 3 254 539 325 . . . .
Report #3:
CUSTOMER PRODUCT STATE PROD_AVG OTHER_CUST_AVG OTHER_PROD_AVG
======== ======= ===== ======== ============== ==============
Helen Bread NY 243 268 1493 Emily Milk NJ 1426 478 926
. . . .
Report #4:
CUSTOMER PRODUCT 1/3 PURCHASED BY MONTH
======== ======= ======================
Emily Butter 2
Bloom Soap 3
. . . .
