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Making QR Code 2d barcode in Software Design and Business Intelligence 383

16 Design and Business Intelligence 383
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Another potential complication occurs when there is more than one way that people might want to compare the same two processes. As with the factless query, a given set of attributes can be related in multiple ways. Even a tool that can drill across cannot be expected to infer what a user wants. Separate merged fact tables can be built for each flavor or comparison, or multiple semantic layers can be constructed for the different forms of comparison. In Figure 16-3, there are two dates associated with each shipment: the date of the shipment, and the date of the order being fulfilled. Either may be used to compare shipments with orders. One option compares orders by order date with shipments by shipment date. This gives an overview of all activities that occurred on each day. The other option compares orders by order date with shipments by order date (rather than shipment date). This gives an overview of the fulfillment status of orders placed each day. The queries to answer these questions involve exactly the same columns of the dimensional schema, but they are constructed in different ways: In the former case, drilling across requires querying quantity_ordered by order date and quantity_shipped by shipment date. These intermediate result sets are then merged based on dates. In the latter case, drilling across requires querying quantity_ordered by order date and quantity_shipped by order date. These intermediate result sets are then merged based on dates. If the user brings date, quantity_ordered, and quantity_shipped to the canvas, a business intelligence tool does not have enough information to choose among these options. This problem may be solved by producing two merged fact tables: one that drills across on order date and one that drills across on order date for orders and shipment date for shipments. Alternatively, two semantic layers can be set up, with the appropriate business views, aliases, and joins to support the two viewpoints. TIP When there are multiple ways to compare two processes, a query generator cannot infer what users want based on the data elements they have requested. Build multiple merged fact tables for each possibility, construct different semantic layers for each possibility, or leave drilling across in the hands of expert developers.
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Multiple stars may bring yet another challenge. It is necessary to understand whether and how your reporting tool supports conformed dimensions. When two distinct dimension tables conform, a single logical attribute can be found in multiple physical locations. This occurs when conformed replicas are stored in different databases, or when one dimension is a conformed rollup of another. In the semantic layer, it is generally preferable to have a single logical dimension attribute. Some reporting tools are unable to comprehend situations like this. In Figure 16-5, for example, dimension attributes such as month, quarter, and year can be found in multiple places. In the orders star, they are found in the day table. In the sales goals star, they are found in the month table. It might be desirable to provide a single set of date attributes in the semantic layer. They can be combined with the goal facts to study
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DAY CUSTOMER ORDER_ FACTS
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. . . order_dollars . . .
day_key full_date holiday_flag month month_number quarter year ...
MONTH
SALES_GOAL_ FACTS
month_key month month_number quarter year ...
. . . goal_dollars . . .
PLAN_ VERSION
Some attributes appear in multiple places
Figure 16-5 Conformed rollups pose another problem for SQL-generating tools
plans, order facts to study actuals, or both to drill across. Unfortunately, many tools are unable to comprehend the possibility that there are multiple possible places to find month (or quarter, or year.) When a business intelligence tool is unable to identify cases where conformed attributes, such as month, can appear in multiple tables, it will not be able to drill across automatically. In this case, drilling across may once again be left to developers who manually construct queries. Alternatively, a separate merged fact table can be provided, eliminating the need to drill across. TIP Conformed dimensions may result in a single logical attribute appearing in multiple physical locations. Some reporting tools can understand this, and generate SQL that fetches the attribute from the appropriate location. If a tool does not have this ability, drilling across will be a manual process. A merged fact table can be added to obviate the need for manual drill-across reports. Reporting tools that cannot identify conformed dimensions may be able to automatically drill across in this situation if the schema is designed as a snowflake, rather than a star. This approach, depicted in Figure 16-6, ensures that each attribute is stored in a single location.
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