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1. Start with your default transaction isolation level set to Read Committed. 2. Examine your first transaction s code for the tables that it reads from and writes to. 3. If you don t care whether the data being read is dirty, if the transaction s modifications could take place anyway despite other concurrently running transactions, and if no further tests require a higher level of isolation, you can reduce the isolation level to Read Uncommitted for that transaction. 4. If you read from the same table twice during the same transaction and whether the first and second reads are different makes a difference to you, increase your isolation level to Repeatable Read on that transaction. 5. If you have any transactions that must not see others rows that are inserted while your transaction is executing, increase the isolation level of that transaction to Serializable. 6. Examine the rest of the transactions in your system by using this technique. Remember that you must individually consider each of your transactions in parallel with all other database operations that could possibly occur at the same time with multiple users doing so is what makes fine-tuning transaction isolation such a difficult task. By the way, many developers just go as far as Step 1 and leave it at that. But if you re going to use your database in a high-transaction throughput system, it serves you well to take the hours necessary to carefully analyze your transactions for their optimum isolation level and dial them in accordingly.

(j 1)(k 1)

As we state in 9, no matter how many tables a relational join traverses, regardless of whether it contains a GROUP BY clause (or anything else for that matter), all query result sets manifest themselves as one or more rows that contain an identical collection of one or more columns. So, in a way, query results are virtual tables based on underlying physical tables of data. Now imagine if you could take a query statement and define it as a formal database object that could be accessed just as a table can. Well, you can that is what is called a view. Listing 10-5 defines a database view.

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Formal elicitation techniques customarily rely on education and training of assessors for reducing bias, under the precept that an assessor conscious of bias can and will compensate for its effects General knowledge questionnaires are sometimes used to demonstrate overcon dence on a personal level However, research ndings on the effectiveness of such debiasing techniques are mixed, with some (Alpert and Raiffa, 1982) showing subjects to be nearly impervious to training even when offered rewards for unbiased results Another perhaps more consistently effective debiasing technique for overcon dence is to request that the assessor specify and list the reasons why an elicited probability might be wrong, for instance by imagining reasons that might explain the occurrence of a less-likely outcome after the fact Encoding refers to the actual speci cation of numerical probability values, and various aids are useful in geotechnical applications.

The boldfaced code in Listing 10-5 is just a standard SELECT statement; the rest of the listing simply encapsulates that SELECT statement as a formal, reusable database object a view with a formal name. Now, whenever you want to see all employees without their salaries, you can simply perform the following:

(18.2)

Similarly, you can perform more elaborate queries on a view, as if it was a table, as follows:

SELECT SSN, Firstname + + Lastname AS Fullname, DateOfBirth FROM vwEmployee WHERE DateOfBirth > 01/01/1960 ORDER BY DateOfBirth DESC

(j 1)(k 1)

Views typically hide elements of data so that what is returned exposes only the data that is needed. The elements being hid can be specific columns of a table, rows in that table that do not satisfy a WHERE clause, or a combination of both. Basically, a view masks unneeded data from your application. These masks can be either vertical or horizontal. A vertical mask shields specific columns of a table from returning and is simply the defined collection of underlying table columns that your view contains. A horizontal mask shields specific rows of a table from returning and is simply the WHERE clause that supplies the filtering criteria for the view. You can combine both vertical and horizontal masking in the same view. Listing 10-6, for example, returns only the SalesOrderID, SaleDate, and OrderTotal columns of only those sales orders with a Status of 20.

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