how to make barcode in vb.net 2010 Drilling Across in Software

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Drilling Across
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When a report requires combining information from more than one fact table, it is called a drill-across report. As you learned in 4, drilling across is a two-step process: 1. Aggregate facts from each star separately, grouping each result set by the same dimensions. 2. Merge the result sets together based on the common dimensions, using full outer join logic.
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ORDER_FACTS day_key product_key customer_key quantity_ordered order_dollars order_id order_line_num
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SHIPMENT_FACTS DAY day_key_shipped day_key_ordered product_key customer_key shipper_key quantity_shipped cost_dollars revenue_dollars margin_dollars order_id order_line_num shipment_id shipment_line_num SHIPPER
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Figure 16-3 Two stars share conformed dimensions
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These two steps may take place inside a single SQL statement or be executed as separate processes. Failure to subdivide the task in this manner risks double-counting a fact. The orders and shipments stars in Figure 16-3, for example, may be used to compare the quantity ordered with the quantity shipped by product. Doing this in a single SQL select statement is likely to result in some double-counting. If there is one order and more than one shipment for a product, the DBMS will create a Cartesian product. The order will be double-counted. This effect was fully explored in 4. Many business intelligence tools can be configured to recognize and avoid this pitfall by drilling across. Query generators may carry out the two-step drill-across operation in various ways. 4 described three common approaches: Issue multiple queries, then merge result sets in the reporting environment. Issue multiple queries that create temporary tables, then issue an additional query that joins the temporary tables. Issue a single SQL statement that performs a full outer join on subordinate queries from each star. Products have a variety of different names for this capability, including multi-pass queries, cross-stitching, multi-SQL, and the like. Some configuration is usually required so that the tool will invoke the mechanism automatically. If the business intelligence tool is unable to drill across, it will not be possible to generate reports that compare two or more business processes. As you learned in 4, these are often high-value reports; the inability to produce them would be a serious blow to the analytic environment. As already mentioned, there is a solution, and it can be incorporated directly into the schema design without compromising any of the best practices you have learned in this book. When faced with a tool that cannot drill across, add a merged fact table to the dimensional design, as depicted in the bottom of Figure 16-4. The merged fact table stores the precomputed results of drilling across. This shifts the burden from the
16 Design and Business Intelligence 381
Business View Order Date Date Month Year Order Info Quantity Ordered . . . ORDER_ DAY (day) PRODUCT_ ORDERED (product) quantity_ordered ORDER_ FACTS ORDER_ CUSTOMER (customer)
Stu dy O
rd er s
Business View Shipment Date Date Month Year Shipment Info Quantity Shipped . . . SHIPMENT_ DAY (day) PRODUCT_ SHIPPED (product) quantity_shipped SHIPMENT_ FACTS SHIP_TO_ CUSTOMER (customer) SHIPPER
Study Shipments
Figure 16-4
reporting process to the ETL process. Queries that compare facts from multiple stars are simplified, since all necessary information now resides in a single star schema. Notice that this solution requires the user to select which star to use for each report. This potentially confusing matter can be simplified by constructing multiple semantic layers one for the study of orders, one for the study of shipments, and one for comparing the processes. The user s choice of star is thereby made before he or she even begins bringing items onto a reporting canvas. TIP A merged fact table can be used to make up for reporting tools that cannot drill across. The addition of a merged fact table may be appealing even when the business intelligence tool is capable of drilling across. Because the work of comparing processes is shifted to the ETL process, the merged fact table is likely to be a better performer. This is particularly true when stars for the individual processes reside in different physical locations or in heterogeneous products. The solution may also be easier for users to understand and work with.
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