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been acquired by IBM), features these extensions as part of its Red Brick Intelligent SQL (RISQL) language: I Ranking. Supports queries that ask for the top 10 and similar requests. I Moving totals and averages. Supports queries that smooth raw data for time series analysis. I Running totals and averages. Allows query responses that show results for individual months plus year-to-date totals, and similar requests. I Ratios. Allows queries that very simply express the ratio of individual values to a total or subtotal without the use of complex subqueries. I Decoding. Simplifies the translation of dimension-value codes (like the supplier-id in the example warehouse) into understandable names. I Subtotals. Allow production of query results that combine detailed and summary data values, at various levels of summarization. Other warehousing vendors provide similar extensions in their SQL implementations or provide the same capabilities built into their data analysis products. As with extensions in other areas of the SQL language, although the conceptual capabilities provided by several different DBMS brands may be similar, the specifics of the implementation differ substantially.

The DBMS then executes the FOR loop repeatedly, by fetching a row of query results at the top of the loop, placing the column values into the CURS_ROW variable, and then executing the statements in the loop body When no more rows of query results are to be fetched, the cursor is closed, and processing continues after the loop..

The performance of a data warehouse is one of the keys to its usefulness. If business analysis queries take too long, people tend not to use the warehouse on an ad hoc basis for decision making. If it takes too long to load data into the warehouse, the corporate IS organization will probably resist frequent updates, and stale data may make the warehouse less useful. Achieving a good balance between load performance and runtime performance is one of the keys to successful warehouse deployment.

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The second problem (UPDATE of the child table) is similarly handled by checking the updated foreign key value If there is no matching primary key value, the UPDATE statement is rejected with an error message In Figure 11-1, this means that before a salesperson can be reassigned to a different office, that office must already be in the OFFICES table Again, this restriction makes sense in the sample database The third problem (DELETE of a parent row) is more complex For example, suppose you closed the Los Angeles office and wanted to delete the corresponding row from the OFFICES table in Figure 11-1 What should happen to the two child rows in the SALESREPS table that represent the salespeople assigned to the Los Angeles office Depending on the situation, you might want to: Prevent the office from being deleted until the salespeople are reassigned.

Automatically delete the two salespeople from the SALESREPS table as well Set the REP_OFFICE column for the two salespeople to NULL, indicating that their office assignment is unknown Set the REP_OFFICE column for the two salespeople to some default value, such as the office number for the headquarters office in New York, indicating that the salespeople are automatically reassigned to that office The fourth problem (UPDATE of the primary key in the parent table) has similar complexity For example, suppose for some reason you wanted to change the number of the Los Angeles office from 21 to 23 As with the previous example, the question is what should happen to the two child rows in the SALESREPS table that represent salespeople from the Los Angeles office Again, there are four logical possibilities: Prevent the office number from being changed until the salespeople are reassigned.

The process of loading a warehouse can be very time-consuming. It s common for warehouse data loads to take hours or even days for very large warehouses. Load processing typically involves these operations: I Data extraction. The data to be loaded into the warehouse database typically comes from several different operational data sources. Some may be relational databases that support OLTP applications. I Data cleansing. Operational data tends to be dirty in the sense that it contains significant errors. For example, older transaction-processing systems may not have strong integrity checks, permitting the entry of incorrect

OLD OPERATION OPERATORS OTHERS PARAMETERS PENDANT PREORDER PRIVATE PROTECTED RECURSIVE REF REFERENCING REPLACE RESIGNAL

In this case, you should first add a new row to the OFFICES table with the new office number for Los Angeles, then update the SALESREPS table, and finally delete the old OFFICES row for Los Angeles Automatically update the office number for the two salespeople in the SALESREPS table, so that their rows are still linked to the Los Angeles row in the OFFICES table, via its new office number Set the REP_OFFICE column for the two salespeople to NULL, indicating that their office assignment is unknown Set the REP_OFFICE column for the two salespeople to some default value, such as the office number for the headquarters office in New York, indicating that the salespeople are automatically reassigned to that office.

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