how to make barcode in vb.net 2010 Part ii in Software

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Part ii
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PART II
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Multiple Stars
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ORDER_FACTS day_key product_key customer_key quantity_ordered
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SHIPMENT_FACTS day_key product_key customer_key quantity_shipped
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Orders vs. Shipments January 2008 Product Product 111 Product 222 Product 333 Quantity Ordered 100 200 50 Page 1 of 1 Quantity Shipped 100 200
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Figure 4-9 A report involving multiple fact tables
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The Peril of Joining Fact Tables
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A dimension table can be thought of as the parent in a parent child relationship with a fact table. If the dimension is related to other fact tables, child rows in each of the fact tables can be thought of as siblings; they share a common parent. For example, a given product may have multiple corresponding rows, or children, in an order_facts table. The same product may also have one or more child rows in shipment_facts. The fact tables at the top of Figure 4-10 illustrate this phenomenon. The product designated by product_key 222 has one corresponding row in order_facts, and two corresponding rows in shipment_facts. The order for product 222 is a sibling of each of the two shipments. When a SQL query attempts to join siblings together, either directly or through a common parent, the RDBMS will match each child from one table with each of its siblings in the other. The result is known as a Cartesian product. This occurs when two fact tables are joined directly together or through a common dimension. In a query summarizing orders and shipments by product, for example, the single order for product 222 will be paired with each of the two shipments for product 222. This has the unfortunate effect of double counting the order. If there were three shipments, it would be tripling the count. The same would happen to a shipment if there were multiple corresponding orders.
4 A Fact Table for Each Process 73
ORDER_FACTS customer_ product_ day_key key key 123 123 123 777 777 777 111 222 333
quantity_ key 100 200 50
SHIPMENT_FACTS customer_ product_ day_key key key 456 456 789 777 777 777 111 222 222
quantity_ shipped 100 75 125
SELECT product.product, SUM( order_facts.quantity ordered ), SUM( shipment_facts.quantity_shipped ) FROM product, day, order_facts, shipment_facts WHERE order_facts.product_key = product.product_key AND order_facts.day_key = day.day_key AND shipment_facts.product_key = product.product_key AND shipment_facts.day_key = day.day_key AND ...additional qualifications on date... GROUP BY product.product The order for product 222 is double counted The order for product 333 does not appear
product ----------Product 111 Product 222
sum(quantity_ ordered) ---------------100 400
sum(quantity_ shipped) ----------------100 200
Figure 4-10
Joining two fact tables leads to trouble
TiP Never attempt to join to two fact tables, either directly or through a common dimension. This can produce inaccurate results. The result of this Cartesian effect is evident in the report at the bottom portion of Figure 4-10. The query attempts to select total orders and total shipments and group them by product. Since product 222 has one order and two shipments within the scope of aggregation, the order is double counted. The resulting report incorrectly shows 400 units ordered. Also notice that product 333 does not show up in the report at all. Although it was ordered, there were no corresponding shipments. The RDBMS was therefore not able to join the order to corresponding shipments. The SQL-literate reader may suggest substituting outer joins to the dimension tables; however, this will not solve the problem as the query has been qualified within the day dimension.
Drilling Across
The proper way to compare two processes is called drilling across. This term has a tendency to cause confusion. Although the word drill is used, this process is unrelated to the drillup, drill-down, or drill-through capabilities of many query and reporting tools. Instead, the term is meant to describe crossing multiple processes. While it is common to speak of a
Part ii
PART II
Multiple Stars
drill-across operation as a drill-across query, the operation is often carried out through more than one query. Drilling across is successfully completed by decomposing the collection of data into discrete steps. The first step summarizes facts from each star at a common level of detail; the second step combines them. The technique can be used on two or more stars, across multiple databases, and even on data stored in RDBMSs from different vendors. You can also use drill-across techniques to query a single star more than once, producing useful comparison reports.
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