how to make barcode in vb.net 2010 Nonadditive facts are computed as the ratio of additive facts in Software

Drawing QR-Code in Software Nonadditive facts are computed as the ratio of additive facts

Nonadditive facts are computed as the ratio of additive facts
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NOTE This is not the end of the story on additivity. In addition to additive and nonadditive facts, you may also encounter facts that are semi-additive. Semi-additivity is discussed in 11, Transactions, Snapshots, and Accumulating Snapshots.
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The level of detail represented by a fact table row is referred to as its grain. Declaring the grain of a fact table is an important part of the schema design process. It ensures there is no confusion about the meaning of a fact table row, and guarantees all facts will be recorded at the same level of detail. Grain may be described in a number of ways. Many schema designers describe grain simply by enumerating the associated dimensions. For example, the grain of order_facts in Figure 3-1 can be described as Orders by Day, Salesperson, Product, and Customer. While this may sound like stating the obvious, it reveals important information about the star. In this case, the statement of grain has the following implication: on a given day, if a customer places multiple orders for the same product with the same salesperson, these orders will be combined into a single row. This aggregation potentially discards useful information. In most situations, schema designers try to avoid aggregating data before loading it into a fact table. By keeping the most granular data possible, the star is able to address a wider variety of analytic needs. Initial business requirements may not call for detailed data, but analytic requirements have a tendency to change. If an aggregated schema is built, future requests for detailed information will require starting over. This guideline holds true regardless of the data warehouse architecture. In a dimensional data warehouse, it is crucial that fact tables capture granular data, since they serve as the central repository for detailed data. If performance becomes a concern, the granular fact table may be supplemented with aggregates, as discussed in 15. The guideline may be relaxed in a Corporate Information Factory architecture, where a separate repository contains granular data. In this scenario, a data mart fact table may aggregate data without fear of losing information. Nevertheless, a future request to study granular data will require redevelopment of the data mart. TIP Set the fact table grain at the lowest level of detail possible. This guideline helps ensure maximum analytic flexibility. It can be relaxed if there is a separate repository for granular data, but may limit future utility. In many cases, a clear statement of grain can be made without reference to the dimension tables in a schema. This form of grain statement is usually preferable, because it ties grain to a business term or an artifact of the business processes. For the orders process, grain might be defined as orders at the order line level of detail. This clearly speaks to the business process and leaves no doubt about the meaning of a fact table row. The design in Figure 3-3 does not meet this definition of grain, but it can be adjusted to do so, as you will see shortly.
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Rows are recorded in fact tables to represent the occurrence of business activities. This means that fact tables do not contain a row for every possible combination of dimension values. The number of combinations that appear in the fact table is relatively small in comparison to the number of possible combinations. This characteristic of fact tables is called sparsity.
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NOTE Some fact tables are denser than others. Some examples will be provided as part of 11 s discussion of snapshot models.
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Degenerate Dimensions
Sometimes, it is not possible to sort all the dimensions associated with a business into a neat set of tables. In situations like this, it may be appropriate to store one or more dimensions in the fact table. When this is done, the dimension column is called a degenerate dimension. Although stored in the fact table, the column is still considered a dimension. Like the dimension columns in other tables, its values can be used to filter queries, control the level of aggregation, order data, define master detail relationships, and so forth. Degenerate dimensions should be used cautiously. Because the fact table accumulates rows at a rapid pace, the inclusion of degenerate dimensions can lead to an excessive consumption of space, particularly for textual elements. In most cases, candidates for degenerate dimensions are better placed in junk dimensions. Transaction identifiers are exceptions to this guideline. TIP Avoid overusing degenerate dimensions. If an attribute is not a transaction identifier, consider placing it in a junk dimension instead. Transaction identifiers are commonly stored as degenerate dimensions. They may also serve as a unique identifier for fact table rows, and define fact table grain. The orders star in Figure 3-3 was criticized for not storing granular data. It can be redesigned to store information at the order line level of detail by adding degenerate dimensions that identify the order and order line. The result is shown in Figure 3-5. The grain of the fact table in Figure 3-5 can be stated as orders at the order line level of detail. This has been achieved by adding transaction identifiers from the source system to identify discrete order lines: the order_id and order_line. Together, these two attributes can serve as a unique identifier for fact table rows. NOTE Although transaction identifiers are commonly stored as degenerate dimensions, this is not a hard-and-fast rule. In some cases, the storage of transaction identifiers in fact tables can be a problem for business intelligence tools. These products sometimes have difficulty generating queries if the same data element is present in more than one table. This situation will be discussed in 16, Design and Business Intelligence. As an alternative to this design, it is possible to construct a dimension table to represent the order line. This dimension table would contain the order number and order line number. It could also contain the attributes shown in the order_info dimension of Figure 3-5. This alternative keeps the degenerates out of the fact table. That may seem useful, but notice that it would not save any space. Because each fact table row represents exactly one order line, the dimension and fact table would contain the same number of rows.
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