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Objectives: |
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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”. |
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Description:
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Generate reports based on the following queries: 1. For each customer, 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. 2. 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 2017 and January of 2018 are considered January regardless of the year. 3. 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, (3) the customer’s average sale for the given state, but for all of the other products, and (4) the customer’s average sale for the given product, but for all of the other states. 4. For each customer, find the top 3 highest quantities purchased in New Jersey (NJ). Show the customer’s name, the quantity and product purchased, and the date they purchased it. If there are ties, show all – refer to the sample output below. 5. For each product, find the median sales quantity (assume an odd number of sales for simplicity of presentation). (NOTE – “median” is defined as “denoting or relating to a value or quantity lying at the midpoint of a frequency distribution of observed values or quantities, such that there is an equal probability of falling above or below it.” E.g., Median value of the list {13, 23, 12, 16, 15, 9, 29} is 15. For example, given the following sales transactions for Bread, the median quant for Bread is 3.
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PRODUCT QUANT
======= =====
Bread 1
Bread 1
Bread 1
Bread 2
Bread 2
Bread 3
Bread 4
Bread 5
Bread 6
Bread 7
Bread 7
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).
You are only allowed to standard SQL syntax covered in class – do not use any other functions other than the 5 aggregate functions (sum, count, avg, max & min); 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:
CUSTOMER PRODUCT MONTH SALES_COUNT_BETWEEN_AVGS
======== ======= ===== ========================
Claire Apple 1 <NULL>
Dan Banana 3 19 . . . .
Report #2:
CUSTOMER PRODUCT MONTH BEFORE_AVG DURING_AVG AFTER_AVG
======== ======= ===== ========== ========== =========
Boo Fish 1 <NULL> 284 434 Sam Eggs 3 254 539 325 . . . .
Report #3:
CUSTOMER PRODUCT STATE PROD_AVG OTHER_CUST_AVG OTHER_PROD_AVG OTHER_STATE_AVG
======== ======= ===== ======== ============== ============== ===============
Helen Bread NY 243 268 493 287 Emily Milk NJ 426 478 926 194 . . . .
Report #4:
CUSTOMER QUANTITY PRODUCT DATE
======== ======== ======= ==========
Boo 982 Banana 2019-03-01
Boo 762 Fish 2016-12-23
Boo 679 Eggs 2017-02-27
Boo 679 Ice 2017-01-15
Chae 999 Butter 2016-11-11
Chae 990 Grapes 2017-08-07 Chae 901 Jellies 2017-07-31 . . . .
Report #5:
PRODUCT MEDIAN QUANT
======= ============
Bread 422
Milk 1976 . . . .
Grading: NOTE:
1. A query with syntax errors will lose 50% of the points for the query.
2. For this course, you are only allowed to use the syntax covered in class (any query
using such syntactic features will result in 0 point) – e.g., do not use aggregate functions other than the 5 (sum, count, avg, max & min); do not use the keywords such as coalesce, limit, row_number, etc. and ‘case’ statement inside aggregate functions. Additionally, do not use any algorithmic features such as ‘if then’, ‘while’, etc.
If you’re unsure, please ask before using any syntactic features that are not covered in class.
Submission: Submit one file containing all the 5 queries on Canvas – Please include your name and CWID in the file. The file type must be “TXT”.
Please include a “README” file if any special instructions are required.
I encourage you to discuss the “ideas” with your TAs (rather than your classmates, esp, if you have any specific questions), but the final queries must be your own work. If I determine that your queries are copies of someone else’s, both you and that someone else will be disciplined (you will receive 0 for the entire assignment) and possibly receive additional penalties for the course.
