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The difference between generic versus specific is very important in database design. For example, common words in natural languages such as book and course have both generic and specific meanings. In spoken language, the precise meaning of these words is normally obvious from the context in which they are used.
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When designing data models, you must be very careful about the distinction between generic and specific meanings of the same word. For example, a course has a title and a duration (generic), while a specific course offering has a location, a certain number of attendees, and an instructor. A specific book on the shelf might have your name and purchase date on the cover page, and it might be full of your personal annotations. A generic book has a title, an author, a publisher, and an ISBN code. This means that you should be careful when using words like course and book for database entities, because they could be confusing and suggest the wrong meaning. Moreover, we must maintain a clear separation between an entity itself at the generic level and a specific occurrence of that entity. Along the same lines, there is a difference between an entity attribute (at the generic level) and a specific attribute value for a particular entity occurrence.
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Redundancy
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There are two types of data: base data and derivable data. Base data is data that cannot be derived in any way from other data residing in the information system. It is crucial that base data is stored in the database. Derivable data can be deduced (for example, with a formula) from other data. For example, if we store both the age and the date of birth of each course attendee in our database, these two attributes are mutually derivable assuming that the current date is available at any moment. Actually, every question issued against a database results in derived data. In other words, it is both undesirable and impossible to store all derivable data in an information system. Storage of derivable data is referred to as redundancy. Another way of defining redundancy is storage of the same data more than once. Sometimes, it makes sense to store redundant data in a database; for example, in cases where response time is crucial and in cases where repeated computation or derivation of the desired data would be too time-consuming. But typically, storage of redundant data in a database should be avoided. First of all, it is a waste of storage capacity. However, that s not the biggest problem, since gigabytes of disk capacity can be bought for relatively low prices these days. The challenge with redundant data storage lies in its ongoing maintenance. With redundant data in your database, it is difficult to process data manipulation correctly under all circumstances. In case something goes wrong, you could end up with an information system containing internal contradictions. In other words, you would have inconsistent data. Therefore, redundancy in an information system results in ongoing consistency problems. When considering the storage of redundant data in an information system, it is important to distinguish two types of information systems: Online transaction processing (OLTP) systems, which typically have continuous data changes and high volume Decision support (DSS) systems, which are mainly, or even exclusively, used for data retrieval and reporting, and are loaded or refreshed at certain frequencies with data from OLTP systems
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In DSS systems, it is common practice to store a lot of redundant data to improve system response times. Retrieval of stored data is typically faster than data derivation, and the risk of inconsistency, although present for load and update of data, is less likely because most DSS systems are often read-only from the end user s perspective.
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