how to make barcode in vb.net 2010 Figure 4-2 A report that focuses on shipping has extraneous data in Software

Painting QR in Software Figure 4-2 A report that focuses on shipping has extraneous data

Figure 4-2 A report that focuses on shipping has extraneous data
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4 A Fact Table for Each Process 65
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be very confusing to end users, who will rightly ask, Why is product 333 showing up on this report We did not ship product 444 either, and I don t see that one on the report. What gives The appearance of product 333 on this report is easily explained: it is there because the customer ordered it during the time period in question. The order caused a row to be recorded in the fact table. Since there was no shipment of that product to that customer on the same day, the quantity_shipped was recorded as 0. This explanation is little consolation to business users trying to analyze shipment data. They are not interested in the fact that orders occurred. To them, these zero-valued facts are a perplexing annoyance. The phenomenon of zero-valued facts showing up on reports is common when multiple processes are modeled in a single fact table. If people want to study only one of the processes, they will encounter what appear to be extraneous rows. This is attributable to a lapse in sparsity. In the case of quantity_shipped, its pairing with quantity_ordered required a row to be recorded when an exclusive focus on shipments would not. Notice that the use of NULL values would not have solved the problem. Product 333 would still appear on the report, only this time with a blank instead of a zero. TiP When two or more facts do not occur simultaneously, they represent different processes. Placing them in a single fact table will hamper analysis of the individual processes. One might be tempted to try addressing the zero-valued result problem at query time rather than schema design time. For example, the query to produce the report in Figure 4-2 might be adjusted to contain a having clause:
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select product_key, sum(quantity_shipped) from sales_facts group by product_key having sum(quantity_shipped) > 0
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In order to work around a shortcoming of the design, the reporting process has been made more complicated. This adjustment to the SQL must be tacked onto every query that studies shipments; a similar clause must be added to every query that focuses on orders. If there is more than one fact associated with each process, it will be necessary to keep track of which clauses to add in each situation. This workaround is an example of something an old friend of mine used to call boiling the frog. You may have heard this story before. If you place a frog into a pot of water on the stove, and turn up the temperature a degree, he will not notice the change. If you continue to increase the temperature by one degree at a time, each change is so small that he will continue not to perceive any changes. Eventually, he will boil to death. In reality, of course, the frog will jump out of the pot at some point. The story, however, illustrates the effect of poor schema design. Report developers are like the frog in the story. Every workaround that they must employ is like a one-degree change in temperature. Each workaround seems small, but their cumulative effect can be unbearable. When studying one process, it will always be necessary to insert the having clause. Other types of queries will be impacted as well counts, averages, subqueries, and correlated subqueries will all be rendered more complicated. Worse, it will also be important to remember that when studying the two processes together, the having clause must be removed. Schema design time, as my friend used to say, is your chance to un-boil the frog.
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SALES_FACTS day_key product_key customer_key fact_type_key generic_fact
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FACT_TYPE fact_type_key fact_name
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PRODUCT
CUSTOMER
query
FACT_TYPE fact_type_key fact_name 1 Quantity Ordered 2 Quantity Shipped
results PRODUCT Product 111 Product 222 FACT_NAME Quantity Shipped Quantity Shipped SUM (GENERIC_FACT) 100 200
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