how to make barcode in vb.net 2010 Guidelines for the Semantic Layer in Software

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Guidelines for the Semantic Layer
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Business intelligence tools offer a tremendous variety of capabilities, all aimed at achieving a rich and valuable range of analytic options. Some of the things you can do with these tools, however, may be better done as part of the ETL process. These features may be useful in making up for shortcomings of existing designs, but when it comes to new dimensional designs, they are best avoided. There are also capabilities that should always be exploited when the opportunity affords it.
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Features to Avoid
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Business intelligence tools allow you to hide the technicalities of the database design from people building reports. As you have seen, configuring a semantic layer replaces a technical with a business view. Some of the features offered may be useful and powerful, but it does not always make sense to use them. Dimensional designs should be rich and understandable. Heavy reliance on the semantic layer to achieve these goals indicates shortcomings in the underlying design. Many BI features that transform a technical view to a business view are best saved for legacy stars. For new designs, your objective should be to require a minimal semantic layer.
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Renaming Attributes
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Business intelligence tools allow the items in the business view to bear different names from those of the underlying database columns. A cryptic column name like cust_addr_line_1 may be translated into something that is more easily understood, such as Address. This allows a more understandable view than what is provided by the physical schema. In the case of a dimensional design, however, most attributes should already be understandable. As you learned in 1, a dimensional model is a representation of the way in which a business views and evaluates a business process. With the exception of surrogate keys and other housekeeping columns, every attribute is either a fact or dimension carrying business significance. In a good design, the names and contents of every attribute will be familiar and understood by business users. A schema designer should not operate on the assumption that cryptic naming schemes can be cleaned up in the business intelligence tool. It is useful to transform column names into neat, mixed-case items without any underscores or the like. Any further transformation in the semantic layer represents a missed opportunity to name the column well as part of the design.
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Part VI
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PART VI
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TIP When designing a dimensional model, choose attribute names that are recognizable and understandable. Do not rely on a business intelligence tool to provide user-friendly translations. This is not to say that this capability of a business intelligence tool should never be used. If an existing schema design falls short in the understandability department, then the capabilities of a business intelligence tool can be used to make up for this fact. It may also be useful to name things differently for different groups of users. When designing a star, however, your goal should be to choose column names that will make obvious sense.
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Creating Virtual Attributes
Like a view in a relational database, a business intelligence tool can be used to combine existing columns to create new items. For example, if a table contains columns for first name and last name, you might use a business intelligence tool to create something called full name. This item simply concatenates the first name, a space, and the last name, perhaps by injecting the following SQL into a generated query:
concat( first_name, ' ', last_name )
A new dimension has been constructed using two existing dimensions. This construct is a virtual attribute. Like a column in a view that does the same thing, it can be used in a query even though it is not actually stored in the database. When a user drags full name onto the canvas and runs the report, the necessary concatenation will occur at query execution time, in the SQL statement that fetches data. This capability may seem useful, since it saves a little space in the dimension table. Remember, though, that a dimension like full name can do more than simply appear on reports. It may also be used to constrain queries, control how facts are aggregated, sort data, or even serve as the basis for drilling across. When used in any of these ways, computing the value as part of the query will hamper performance often seriously. When developing a dimensional design, your goal should be to anticipate useful permutations of data elements and incorporate them directly into the schema design. Including an element like full name in the dimension table, rather than computing it at query time, will vastly improve performance. The burden of computing it is shifted from the DBMS to the ETL process, and it will be possible for database administrators to index it for optimal performance. Incorporating the dimension attribute directly into the schema design also makes it available to other tools in your architecture that may be used to develop reports, guaranteeing that it is computed consistently. TIP Do not rely on a semantic layer to save space. The dimensional design should include any useful combinations of data elements, even if they are redundant. This guideline extends beyond simple concatenations. As you learned in 3, the dimensional design should include useful combinations of attributes, include text values that correspond to standard codes, express flags in readable terms rather than Boolean Yes/No values, and so forth. You can use a business intelligence tool to cause these transformations of data to take place at query time, but doing so will hamper performance. Furthermore, these
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