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For instance, in developing a database for a college, entity types would include students, professors, dormitory buildings, classroom buildings, majors, courses, etc. Attributes of a student would include name, address, dorm, room number, major, advisor, etc. One relationship between entity types would be the advisor/ advisee relationship between a professor and a student. Entities are the things, the nouns, the database will store. Often entity types correspond to classes of real-world objects, such as professors, cars, and buildings. Sometimes entity types correspond to more abstract objects, like a college within a university, an order for an on-line bookstore, and a privilege afforded a group of users. A big part of data modeling is deciding which entity types to model. For those familiar with object-oriented programming concepts, an entity type is similar to a class. Each individual entity of an entity type (think of an instance of a class) will be characterized by a set of attribute values. Attributes are the adjectives or descriptors of the entities the database will store. For instance, extending the example from two paragraphs above, a particular student could have the attributes Bill Smith, Akron, OH, Fisher Dorm, 323, Computer Science, Professor Findley, etc. The structure of a database is described by its schema. As we will see later, in order to convert the data model to a relational database schema, each entity instance of an entity type must be unique. Something in the set of attributes must make each entity different from all other entities of the same type. Returning to the example in the previous paragraph, we expect only one Bill Smith, and if there are two or more, we will find a way to make the different Bill Smith entities unique. We will assign a key to each entity of the student entity type such that we can distinguish each student. Having selected the entity types to include in the database, the data modeler then specifies the relationships among the entities. For instance, we mentioned previously the advisor/advisee relationship between professors and students. One of the important decisions to make is whether the relationship will be 1:1 (one-to-one), 1:N (one-to-many), or N:M (many-to-many). These ratios are called cardinality ratios. In the case of the advisor/advisee relationship, the designer might decide the relationship is 1:N, with 1 advisor advising N students. On the other hand, if the school assigns multiple advisors to each student (for instance, one for the student s major field and one for student life questions), the relationship could be defined as N:M, multiple advisors for each student, and multiple students for each advisor. Another pair of decisions related to the cardinality ratio of a relationship is the specification of minimum cardinalities. Must a student have an advisor If so, then the minimum cardinality on the professor side of the advisor/advisee relationship must be 1. If not, then the minimum cardinality on the professor side of the relationship will be 0; a student entity may exist who is not associated with any advisor. Likewise, must every professor be an advisor If so, then the minimum cardinality on the student side of the advisor/advisee relationship must be 1. If not, then the minimum cardinality on the student side will be 0; a professor entity can exist with no associated advisees. Other relationships might be 1:1. Imagine an entity type called Parking Permit, and that the policy is to allow each student one and only one parking permit. The relationship between student and parking permit could be called parks/permit-to-park, and the relationship is 1:1. The minimum cardinality on the student side would probably be 1, since otherwise it would mean the database tracks parking permits that are not issued to anyone. The minimum cardinality on the parking permit side would probably be 0, since some students probably will not have cars to park. A many-to-many, N:M, relationship would exist between students and courses. We could call this relationship takes/is-taken-by. Each student will take many courses, and many students will take each course. The minimum cardinality for both sides of the relationship will be 1, because each student will certainly take some courses and - each course will be attended by some students. On the other hand, if we keep courses in the database that are no longer actively taught for some reason, then the minimum cardinality on the student side of the takes/is-taken-by relationship will be 0. Figure 8-1 shows a data model for the entities and relationships we have been discussing, using one of many standard approaches for graphically representing the entity-relationship diagram. Figure 8-1 was created using Microsoft Visio. The rectangles represent entities, and the label in the upper portion of an entity rectangle specifies the identifier, or key, for the entity type. For dormitories, for example, the dorm name is the identifier; the name of the dorm distinguishes the record of one dorm from that of another. The labels in the lower portion of the entity rectangles represent the other attributes of the entity. The dorm entity includes information for each dorm about the total number of rooms in the dorm, the number of vacant rooms in the dorm, and the room rental rate for the dorm.
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