Figure 17-7: Using the Layout Wizard to create a paper space layout with floating viewports. in .NET

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The two most important design decisions that can be made concern the granularity of data and the partitioning of data. For most organizations, a dual level of granularity makes the most sense. Partitioning of data breaks it down into small physical units. As a rule, partitioning is done at the application level rather than at the system level. Data warehouse development is best done iteratively. First, one part of the data warehouse is constructed, and then another part of the warehouse is constructed. It is never appropriate to develop the data warehouse under the big bang approach. One reason is that the end user of the warehouse operates in a discovery mode, so only after the warehouse s first iteration is built can the developer tell what is really needed in the warehouse. The granularity of the data residing inside the data warehouse is of the utmost importance. A very low level of granularity creates too much data, and the system is overwhelmed by the volumes of data. A very high level of granularity is efficient to process, but precludes many kinds of analyses that need detail. In addition, the granularity of the data warehouse needs to be chosen in an awareness of the different architectural components that will feed off the data warehouse. Surprisingly, many design alternatives can be used to handle the issue of granularity. One approach is to build a multitiered data warehouse with dual levels of granularity that serve different types of queries and analysis. Another approach is to create a living sample database where statistical processing can be done very efficiently from a living sample database. Partitioning a data warehouse is very important for a variety of reasons. When data is partitioned it can be managed in separate, small, discrete units. This means that loading the data into the data warehouse will be simplified, building indexes will be streamlined, archiving data will be easy, and so forth. There are at least two ways to partition data at the DBMS or operating system level and at the application level. Each approach to partitioning has its own set of advantages and disadvantages. Each unit of data in the data warehouse environment has a moment associated with it. In some cases, the moment in time appears as a snapshot on every record. In other cases, the moment in time is applied to an entire table. Data is often summarized by day, month, or quarter. In addition, data is created in a continuous manner. The internal time structuring of data is accomplished in many ways.
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The hypothesis (28) is rejected if F(int) > F0.25,I 1,N I 1, where F0.25,I 1,N I 1 is the upper percentile of the F distribution with I 1 and N I 1 degrees of freedom. Thus, if the null hypotheses (15) and (28) are not rejected at the 0.25 level of signi cance, the batches can be considered to come from a single population or pool and a single shelf life is computed based on the studied batches. If that is the case, model (14) reduces to the expression yij = + xij + ij i = 1, . . . , I j = 1, . . . , ni (45)
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The previous example of the after returning advice is defined in the exact same way as the before advice, except that this advice executes only after the join point has completed execution. As demonstrated next, this particular advice supports an additional attribute named returning.
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