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In this section of the chapter, we ll model our example in a method known as logical modeling. The purpose of the logical model is to show the data that the application must store to satisfy business requirements. It demonstrates how this data is related and explores any integration requirements with business areas outside the scope of the development project. It is created without any specific computer environment in mind, so no optimization for performance, data storage, and so forth is done. In logical modeling, the term entity is used to mean a conceptual version of a table. As we re still in the logical modeling stage of designing our database, I ll use entity rather than table in this discussion, since it is less tied to implementation. Also within logical modeling, a column of data is referred to as an attribute. To build our logical model, we ll take the information gathered previously in the chapter and implement attributes in our entities. From that, we ll see how we need to alter our design. The question remains, what should be contained in an entity Three principles should govern the contents of an entity: Each entity should have a unique identifier. Only store information that directly relates to that entity. Avoid repeating values or columns. The sections that follow provide more detail about each principle.



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It must be possible to find a unique row in each entity. You can do this through the use of a unique identifying attribute or the combination of several attributes. However, no matter which method you use, it must be impossible for two rows to contain the same information within the unique identifying attribute(s). Consider the possibility that there is no combination of attributes in an entity that can make a row unique, or perhaps you wish to build a single value from a single attribute. SQL Server has a special data type, called unique identifier, that can do this, but a more common solution is to build a column attribute with an integer data type, and then set this up as an identity column. You ll learn more about this technique when building the tables in 5.





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Divide the values in two columns Multiply the values in two columns and subtract the product from the first column Multiply the value in a column by a constant Add the value in two columns and multiply by a third Subtract one date from another Compare the values in two columns

It can be very easy in certain situations to have too much information in one entity and therefore almost change the reason for the existence of the specific entity. Doing so could reduce efficiency in an OLTP system, where duplicate information has to be inserted. It could also lead to confusion when an entity that has been designed for one thing actually contains data for another.

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Built on top of the CLR is the .NET Framework base class library. Included in this class library are reference types and value types that encapsulate access to the system functionality. Types are data structures. A reference type is a complex type for example, classes and interfaces. A value type is simple type for example, integer or Boolean. Programmers use these base classes and interfaces as the foundation on which they build applications, components, and controls. The base class library includes types that encapsulate data structures, perform basic input/output operations, invoke security management, manage network communication, and perform many other functions.

Having attributes of data where the information is an exact copy of another attribute within either the same entity or a related entity is a waste of space and resources. However, what tends to happen is that you have repeated values or attributes within two or more tables, and therefore the information is duplicated. It is in this scenario that you are expected to avoid the repeating values and move them elsewhere.

=[TotalCost]/[Quantity]

Now that you know what should be contained within an entity, how do you go about normalizing the data The normalization forms addressed within this chapter are as follows: First normal form (1NF) Second normal form (2NF) Third normal form (3NF) There are a number of other, higher normal forms, but they are rarely used outside academic institutions, so they will not be covered here.

To achieve 1NF within a database, it is required that you eliminate any repeating groups of information. Any groups of data found to be repeated will be moved to a new table. Looking at each table in turn, we find that we have two tables in our example database that potentially flout the first requirement of 1NF: customers and shares.

=Price-[Price]* [DiscountPercent]

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