how to make barcode in vb.net 2010 Figure 4-3 Modeling a generic fact complicates cross-process analysis in Software

Printing Quick Response Code in Software Figure 4-3 Modeling a generic fact complicates cross-process analysis

Figure 4-3 Modeling a generic fact complicates cross-process analysis
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A variation on the single fact table solution is to record only one generic fact. A new dimension will determine whether it represents an order or a shipment. An example of this technique is shown in Figure 4-3. This approach solves the zero-valued row problem, because each row is either an order or a shipment. Notice that queries must still qualify on the type of fact to be studied. In addition, the query results will show up in an unusual format that will require additional formatting inside reports. This format becomes even more problematic for queries that compare both processes. Information desired in a columnar format will be returned in different rows. Once again, design shortcomings will cause suffering among report developers.
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Modeling in Separate Fact Tables
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Rather than come up with other ways to boil the frog, the preferred solution is to model each process in its own fact table. In the case of orders and shipments, an example is provided in Figure 4-4. Notice that each fact table shares the common dimensions day, product, and customer. Each stores only the appropriate facts; there is no need to record extraneous zeros. Set up in this manner, it is now possible to study the individual process without additional bother. Those seeking information about orders can use order_facts; those seeking information about shipments can use shipment_facts. If either of these processes has additional facts, they can be added to the appropriate fact table. There is another matter to be considered: now that the facts are recorded in separate fact tables, how does one study them together Before we get to that question, let s take a look at the other clue that two facts describe different processes.
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4 A Fact Table for Each Process 67
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ORDER_FACTS day_key customer_key product_key quantity_ordered
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SHIPMENT_FACTS day_key customer_key product_key quantity_shipped
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ORDER_FACTS day_key 123 123 123 customer_ product_ key key 777 777 777 111 222 333 quantity_ ordered 100 200 50
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SHIPMENT_FACTS day_key 456 456 789 customer_ product_ key key 777 777 777 111 222 222 quantity_ shipped 100 75 125
Figure 4-4
Separating the two processes into separate fact tables with shared dimensions
Facts that have Different Grain
When two or more facts describe events with differing grain, they describe different processes. As with facts of differing timing, if they are placed in a single fact table, analysis of the individual processes can be hampered. Placing them in separate fact tables allows each process to be studied more easily. To understand how this happens, the orders and shipments example will be extended. Presume that business requirements for the sales department have been identified and are expressed as follows: Analyze Quantity Ordered by Date, Customer, and Product Analyze Quantity Shipped by Date, Customer, Product, and Shipper These requirements differ from the previous example in one critical aspect: quantity_ shipped comes with an additional dimensional detail: the shipper. This information only applies to shipments, not to orders.
A Single Fact Table Causes Difficulties
The star schema at the top of Figure 4-5 attempts to address the requirements using a single fact table: sales_facts. It contains the facts quantity_ordered and quantity_shipped. The grain of this fact table requires that orders, shipments, or both be recorded by day, product, customer, and optionally shipper. As you have already seen, the presence of or in a statement of grain is a sign of problems to come. This time, other complications exist as well. This star differs from the one shown in Figure 4-1 in that it includes a shipper dimension. In order to deal with situations where a day, product, and customer combination has orders but no shipments, this design adds a special row to the shipper dimension. This row, as
Part ii
PART II
Multiple Stars
SALES_FACTS day_key customer_key product_key shipper_key quantity_ordered quantity_shipped SHIPPER shipper_key shipper_name shipment_type ...
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PRODUCT
SALES_FACTS day_key 123 123 123 456 456 789 customer_ product_ key key 777 777 777 777 777 777 111 222 333 111 222 222 shipper_ key 0 0 0 9999 9999 8888 quantity_ ordered 100 200 50 0 0 0 quantity_ shipped 0 0 0 100 75 120
SHIPPER shipper_ key 0 777 8888 9999 shipper_ name [Not a shipper] US Ship Co. US Ship Co. Zig Zag shipment_ type N/A Overnight Standard Overnight
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