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Aggregate data for these higher levels of the geographic dimension can then be obtained by summary queries that join the fact table to the dimension table and selected based on the district or region columns This approach is conceptually simple, but it means that all aggregate (summary) data must be calculated query by query This likely produces unacceptably poor performance I Multiple levels within the dimension tables The geographic dimension table might be extended to include rows for offices, districts, and regions Rows containing summary (total) data for these higher-level dimensions are added to the fact table when it is updated This solves the runtime query performance problem by precalculating aggregate (summary) data However, it complicates the queries considerably Because every sale is now included in three separate fact table rows (one each for office, district, and region), any totals must be computed very carefully.

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Other variants of these loop-processing constructs are provided by the various dialects, but the capabilities and syntax are similar to these examples.

Some stored procedure dialects provide statements to control looping and alter the flow of control. In Informix, for example, the EXIT statement interrupts the normal flow within a loop and causes execution to resume with the next statement following the loop itself. The CONTINUE statement interrupts the normal flow within the loop but causes execution to resume with the next loop iteration. Both of these statements have three forms, depending on the type of loop being interrupted:

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Specifically, the fact table must usually contain a level column to indicate the level of data summarization provided by that row, and every query that computes totals or other statistics must include a search condition that restricts it to rows at only a specific level I Precomputed summaries in the dimension tables Instead of complicating the fact table, summary data may be precomputed and stored in the dimension tables (for example, summary sales for a district stored in the district s row of the geographic dimension table) This solves the duplicate facts problem of the previous solution, but it works only for very simple precomputed amounts The precalculated totals don t help with queries about products by district or district results by month, for example, without further complicating the dimension tables I Multiple fact tables at different levels.

exit for; exit while; exit foreach; continue for; continue while; continue foreach;

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Instead of complicating the fact table, this approach creates multiple fact tables for different levels of summary data To support cross-dimension queries (for example, district results by month), specialized fact tables that summarize data on this basis are needed The resulting pattern of dimension tables and fact tables tends to have many interrelationships,.

In Transact-SQL, a single statement, BREAK, provides the equivalent of the Informix EXIT statement variants, and there is a single form of the CONTINUE statement as well. In Oracle, the EXIT statement performs the same function as for Informix, and there is no CONTINUE statement. Additional control over the flow of execution within a stored procedure is provided by statement labels and the GOTO statement. In most dialects, the statement label is an identifier, followed by a colon. The GOTO statement names the label to which control should be transferred.

creating a pattern resembling a snowflake; hence, this type of schema is often referred to as a snowflake schema. This approach solves the runtime performance problem and eliminates the possibility of erroneous data from a single fact table, but it can add significant complexity to the warehouse database design, making it harder to understand. In practice, finding the right schema and architecture for a particular warehouse is a complicated decision, driven by the specifics of the facts and dimensions, the types of queries frequently performed, and other considerations. Many companies use specialized consultants to help them design data warehouses and deal with exactly these issues.

CHARACTER_LENGTH ESCAPE CHECK CLOSE COALESCE COLLATE COLLATION COLUMN COMMIT CONNECT CONNECTION CONSTRAINT CONSTRAINTS CONTINUE CONVERT EXCEPT EXCEPTION EXEC EXECUTE EXISTS EXTERNAL EXTRACT FALSE FETCH FIRST FLOAT FOR FOREIGN

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