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The modi cation (F7) gives excellent results (for continuous non-analytic functions one usually has an accuracy two or three gures less than machine precision) To illustrate this, we apply the modi ed approach to the Laplace transform (F6) for the M/D/1 queue with service time D = 1 and traf c intensity = 08 In Table F2 the values of f (t) = P {Wq > t} are given for t = 1, 5, 10, 25 and 50 The results in Table F2 are accurate in all displayed decimals (13 to 15 decimals) The calculations were done with M = 64, = 1, m = 4M, b = 22/m and n = 8 The inverse discrete FFT method was used to compute the g from (F4) In sharp contrast with the accuracy of the modi ed approach (F7), I found for the M/D/1 example the values 055607 and 055527 for P {Wq > t} with t = 1 when using the unmodi ed Den Iseger inversion algorithm and the Abate Whitt algorithm These values give accuracy to only three decimal places In the Abate Whitt algorithm I took a = 191, m = 11 and n = 38 (I had to increase n to 5500 to get the value 05548948 accurate to ve decimal places) The M/D/1 example shows convincingly how useful is the modi cation (F7) A scaling procedure In applied probability problems one is often interested in calculating very small probabilities, eg probabilities in the range of 10 12 or smaller In many cases asymptotic expansions are very useful for this purpose, but it may also be possible to use Laplace inversion with a scaling procedure Such a scaling procedure was proposed in Choudhury and Whitt (1997) The idea of the procedure is very imple Suppose that the function f (t) is non-negative and that the (very small) function value f (t0 ) is required at the point t0 > 0 The idea is to transform f (t) into the scaled function fa0 ,a1 (t) = a0 e a1 t f (t), t 0.

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Translate fact values for ease of use: The business measures needed for reports may be different from how the data is stored in source systems It is helpful to convert data to a common unit of measure for reporting If a sales transaction records the sale of one case, this may be a case of 24 individual units or perhaps 36 individual units Converting all of the sales to the individual units ensures consistent and meaningful reporting Additionally, many facts work together For example, the number of units sold, price, and dollar amount sold are all useful facts Only two of these need to be stored physically; the third can be calculated on-the- y To make this as fast as possible, it is helpful to store the two facts that are used most often Unravel source system logic: In many instances, the source system has core data stored in a manner to help that system run fast and enable each module to be relatively self-contained This may mean that the pieces that are needed to get a complete picture of a sales transaction may be stored in several places or tables To make things more challenging, there may be no direct links between these tables, although there may be a series of translation and lookup tables that are needed to get to the bottom of things This may be why the data is so hard for you to use in the source system The etails are all there, but you need to understand the secret handshakes in order to get to the real numbers The ETL system can apply this logic to provide the real sale numbers to be loaded into a fact table This is done once and then everyone can access these facts without having to learn all of these details Perform complex calculations: Many calculations can be done when creating a report or accessing data in an ad hoc manner However, some calculations may be too complex and long-running to perform on-the- y The ETL system can perform these calculations and store the results as facts, which can then be directly accessed for reporting Figure 10-2 shows a general data ow diagram to build the sales fact table just described In addition to the functions described here, a great deal of work is also required to address anomalies and challenges in the data Often these challenges surface when the ETL system is being developed No matter how carefully the data is pro led in advance or how much detail is included in the design, new and unexpected things will show up in the data The goal is to design the ETL system to be able to gracefully identify and address the unexpected.

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