how to make barcode in vb.net 2010 Part II in Software

Generation QR in Software Part II

Part II
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The two product dimension tables have many differences, any one of which can foil an attempt to drill across. First, consider differences in the structure of the dimension tables.
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PART II
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Multiple Stars
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one star takes into account packaging differences, while the other does not. Timing may also get in the way. If one star collects data on a monthly basis, while the other does so on a weekly basis, there would be virtually no way to compare this data. Weeks and months cannot be rolled up to any common level of summarization. Last, reliance on these workarounds depends on some consistency in the content of the two versions of the dimension. If there are also content differences, it may become impossible to overcome structural differences.
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Differences in Dimension Content
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In the case of the stars in Figure 5-2, further difficulties are evident when you examine the content of the product dimension tables: Product names and categories are formatted differently. The orders star uses mixed case and punctuation; the returns star formats data in all caps without punctuation. These differences will get in the way during the merge phase of drilling across since these values are the basis of the merge. Names are not consistent. SKU 3333-01 is called 9 12 bubble mailer in the orders star, and STANDARD MAILER in the returns star. It may be that a change in product name was handled as a type 1 change for orders and was ignored for returns. The inconsistent names will impede the merging of intermediate result sets for queries that involve the product name. The product with the natural key 4444-22 has one row in the orders star but two rows in the returns star. It appears that this product underwent a change in category that was treated as a type 1 change in the orders star, and a type 2 change in the returns star. It is possible to compare orders and returns by category, but the orders will skew toward the more recent value. The product with SKU 6666-22 is present in the orders star but not in the returns star. This will not impede drilling across, but is an indicator that inconsistencies exist between the tables. The product with SKU 5555-22 is assigned different surrogate key values in the two stars. Care must be taken when joining tables. Again, it may be possible to work around some of these limitations, but the impact on the reporting process would be severe, and not all the issues can be overcome. For example, developers might try to address the first limitation by converting all text to uppercase and stripping punctuation before joining two intermediate result sets together. This will have a negative impact on query performance, since the product name for each granular fact must be adjusted and sorted prior to aggregation. This will not help in situations where product names are recorded differently, as is the case with product 3333-01. Some of the limitations might be dealt with by only referring to SKUs when querying each star, then using a single dimension table to determine the associated dimension values. The facts from each star could then be aggregated before merging intermediate
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5 Conformed Dimensions 91
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Preliminary Requirements for Conformance
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To support successful drill-across comparisons, designers must avoid incompatibilities like those in Figure 5-2. The issues that rendered the two product dimension tables incompatible can be addressed by requiring that the two tables be the same. As noted in 4, there are two crucial parts to this sameness: the tables must be the same in structure and in content.
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Same Structure Structurally, the tables should have the same set of dimension columns. This avoids the need to piece together missing information such as the product_type column. Corresponding dimension columns should have the same names so there is no ambiguity in where their equivalencies lie. They should also have the same data type definitions since their content will be identical. These structural equivalences support the first phase of a drill-across operation. The dimension columns can be relied upon to define a consistent scope of aggregation for each fact table. In the first phase of drilling across, each fact table can be queried using the same dimensional groupings, without the need for a special processing workaround. Additionally, the structural compatibility supports a successful merge in the second phase, although content will also play a crucial role. Same Content In terms of content, the values found in dimension columns must be expressed identically. If the name of product 3333-01 is 9 12 bubble mailer in the orders star, it should be 9 12 bubble mailer in the returns star. This common value will allow intermediate results from each star to be joined during the second phase of a drill-across operation. Use of consistent value instances avoids the need to clean up or convert corresponding column values so that they match, and guarantees that values will support the merge of intermediate results. Corresponding dimension tables should provide consistent results when substituted for one another. In terms of content, this requires that they contain the same set of rows, that corresponding rows share the same surrogate key values, and that slow change processing rules have been applied consistently. These requirements, however, do not apply in cases where the corresponding dimension tables describe different levels of summarization.
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