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- 15 -

You may have been confused by the date conditions in the first SELECT statement of the MINUS query in this section. These conditions are needed to identify customer assignments that were in effect for any portion of the period. Because conditions like these can easily be botched, many designers choose to create an attribute that identifies quarters as a series of integers, assigning the column a name like quarter_number. The first quarter recorded in the database is assigned the value 1, the second the value 2, the tenth the value 10, and so forth. When you are looking for date ranges that overlap with a particular quarter, this trick enables simplification of the necessary constraints. For example, if Q1 2009 is quarter_ number 15, the date constraints in the first half of the MINUS query can be replaced with:

The main issues faced by Java the Hutt are related to its distributed store model. Tracking sales information is important and is one of the reasons for the company s recent success. To meet this need, each Java the Hutt store has a single server that runs a proprietary database application. Store employees depend on this system as a cash register, and workers at the corporate office depend on this data for real-time performance analysis. Generally, it s a well-oiled machine, but one area that has been neglected is data protection. Although data is replicated from the stores to the corporate office, data loss at the store level is not uncommon. Java the Hutt has a formal IT department that is responsible for

The same technique can be used to numerically identify calendar months. The downside to this technique, of course, is that you must know that Q1 2009 is the 15th quarter. Another way to simplify this kind of query is to construct a factless snapshot to model customer assignment, as described earlier in the boxed text titled Snapshots of Conditions.

the orders placed by those customers, the salespeople who sell the products to customers, and the sales offices where those salespeople work. This database, like most others, is a model of the "real world." The data stored in the database represents real entities customers, orders, salespeople, and offices. There is a separate table of data for each different kind of entity. Database requests that you make using the SQL language parallel real-world activities, as customers place, cancel, and change orders, as you hire and fire salespeople, and so on. Let's see how you can use SQL to manipulate data.

these types of issues, but a lack of funding for this department has led to less-than-optimal data protection decisions.

Another way to identify customer assignments without orders is to use a correlated subquery. In looking for customer assignments with no corresponding orders, this might work as follows. A standard query is used to identify customer/salesrep assignments. In the WHERE clause, the keywords AND NOT EXISTS are used to introduce a subquery that looks for order facts for the salesperson/customer pair. The essential structure looks like this:

First, let's list the sales offices, showing the city where each one is located and its yearto-date sales. The SQL statement that retrieves data from the database is called SELECT. This SQL statement retrieves the data you want: SELECT CITY, OFFICE, SALES FROM OFFICES CITY -----------Denver New York Chicago Atlanta Los Angeles OFFICE -----22 11 12 13 21 SALES ----------$186,042.00 $692,637.00 $735,042.00 $367,911.00 $835,915.00

Monica brought the noisy bunch of Java the Hutters to order by clearing her throat It was time for the weekly operations meeting, and all of the big wigs were there The atmosphere seemed somewhat tense, and for good reason I guess we all have an idea of what happened at Store 188, she began It seems that a failed server prevented employees at that location from entering new sales It took over four hours to get a replacement machine to the store Then, it took another two hours to get the machine back up and running Most of the faces looked pretty grim as people stared into their gourmet-coffee cups Neil ran some numbers in his head estimating the amount of revenue from loss of business But there was more The worst part, however, was that we lost a lot of important sales information for the past two days.