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Careful definition of the attribute, however, reveals it is a type 1 attribute: the age at signing is the difference between the effective date of the policy and the birth date of the customer This definition avoids making use of an age column in the source system Frozen attributes like this often appear in conjunction with similar attributes designated for type 1 and type 2 response Age_at_signing, for example, may be accompanied by current_age (type 1) and historic_age (type 2) The source of these attributes can be an age record in the source system Sometimes a frozen attribute is actually exhibiting a combination of type 1 and type 2 behaviors This often occurs in situations where the dimensional model supports statutory reporting In these cases, there may be attributes that can be updated until information is externally reported, after which they must not be updated.

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Subsequent changes must generate a new row The frozen attribute is actually exhibiting both type 1 and type 2 behavior A type 1 response occurs if the old value has not been externally reported; a type 2 response occurs if it has..

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Another way to optimize slow change processing is to simplify it. As Figure 17-2 illustrated, as many as three different lookups or comparisons may be necessary for each source record: looking for new records (step 1.4), looking for type 1 changes (step 1.5), and looking for type 2 changes (step 1.7). Each of these lookups requires comparing one or more columns of a source record to corresponding records in the dimension table.

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The potential economic cost could be very large or very small In some cases that cost may be zero, which is why start-ups and companies with poor-performing stocks object to a fair value expense determined at the grant date On the other hand the true economic cost could greatly exceed the grant date expense Using an example of an option with a $10 exercise price, the Black-Scholes option pricing model would generate a value and expense as of the grant date of $3 to $5 per share However, if the stock price goes to $20 a share, then the potential economic cost to the company is the spread between the market price and the exercise price, or $10 If the stock goes to $50, the economic cost is $40 However, if the stock price fell to $5 a share, the economic cost would be zero.

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Developers may be able to squeeze performance out of the process by eliminating one or more of these lookups. For example, new records are handled in much the same way as type 2 changes: by inserting data into the table. It is possible to forgo the first lookup, allowing new records to be processed through the same steps that identify type 2 changes. Similarly, developers may be able to save time by assuming that all source records contain type 1 changes, skipping the comparison. Issuing an update for each source record may save time if a large number of type 1 changes are expected, or if the database has an upsert capability that can combine this with processing new records.

What about a higher-priced stock Texas Instruments Starting in January 1999, at $22.29 per share, the price doubled by October and nearly doubled again in February 2000 (Figure 2-2). Wow! That s terrific. It s truly amazing to see any stock price double in that short a time. The trouble is, it wasn t stable. As part of the Technology sector, it was a bubble about to burst. The price then fell nearly as fast as it rose. Technology stocks fell and brought the market down with them. The stock trader who got out fast enough made good money. The buy and hold investor became worried and frustrated. It is interesting to note that Texas Instruments runs a beta of 1.8, nearly twice as volatile as Onyx. The price decline in this situation was probably more related to the weakness in the Technology sector and the market as a whole. But the 1.8 beta should have given some cautionary warning of lack of stability. (Beta is a measure of volatility. A beta of 1 is equal to the market in volatility. A beta of 2 is twice as volatile.)

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