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1.1 Types of model

When the word model is used, you are most likely to bring to mind physical models such as those that are constructed to depict new buildings, cars or other artefacts. Such models are a precursor to actually building the artefact ‘for real’. However, our use of the word goes beyond physical models. For example, when a new house is built there will be a variety of plans produced to show different aspects of the house: its floor plan, a diagram of its location, a drawing of the front elevati
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Conclusion

Relational database systems underpin the majority of the managed data storage in computer systems. In this course we have considered database development as an instance of the waterfall model of the software development life cycle. We have seen that the same activities are required to develop and maintain databases that meet user requirements.


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1.6.1 Realising the design

So far we have been concerned only with the specification of a logical schema. We now need our database to be created according to the definitions we have produced. For an implementation with a relational DBMS, this will involve the use of SQL to create tables and constraints that satisfy the logical schema description and the choice of appropriate storage schema (if the DBMS permits that level of control).

One way to achieve this is to write the appropriate SQL DDL statements into a f
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1.5 Design

Database design starts with a conceptual data model and produces a specification of a logical schema; this will usually determine the specific type of database system (network, relational, object-oriented) that is required, but not the detailed implementation of that design (which will depend on the operating environment for the database such as the specific DBMS available). The relational representation is still independent of any specific DBMS; it is another conceptual data model.

Our
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1.4 Analysis

Data analysis begins with the statement of data requirements and then produces a conceptual data model. The aim of analysis is to obtain a detailed description of the data that will suit user requirements so that both high and low level properties of data and their use are dealt with. These include properties such as the possible range of values that can be permitted for attributes such as, in the Open University example for instance, the course code, course title and credit points.
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1.3 Requirements gathering

Here we are concerned only with the requirements that relate specifically to the data. Establishing requirements involves consultation with, and agreement among, all the users as to what persistent data they want to store along with an agreement as to the meaning and interpretation of the data elements. The data administrator plays a key role in this process as they overview the business, legal and ethical issues within the organisation that impact on the data requirements.

The data req
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1.1.1 Desirable properties of a database

As you will see, there are many possible choices that can be made during the design and many rules to guide this work. When trying to decide if some choices are better than others, you need to consider the key desirable properties of a database. The table here outlines some of them:

Completeness Ensures that users can access the data the
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Learning outcomes

After studying this course, you should be able to:

  • describe the key points of the waterfall model applied to database development

  • appreciate the roles of various development artefacts, such as the data requirements document, conceptual data model and such like used to communicate between activities in the database development life cycle

  • communicate effectively about aspects of the development of databases.


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