1. Describe three traditional techniques for collecting information during analysis. When might one be better than another? 2. What are the general guidelines for collecting data through observing workers?
3. What is the degree of a relationship? Give an example of each of the relationship degrees illustrated in this chapter.
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After studying this chapter, you should be able to:
Concisely define each of the following key data-modeling terms: conceptual data model, entity-relationship diagram, entity type, entity instance, attribute, candidate key, multivalued attribute, relationship, degree, cardinality, and associative entity. ■
Ask the right kinds of questions to determine data requirements for an information system. ■
Draw an entity-relationship (E-R) diagram to represent common business situations. ■
Explain the role of conceptual data modeling in the overall analysis and design of an information system. ■
Distinguish between unary, binary and ternary relationships, and give an example of each. ■
Distinguish between a relationship and an associative entity, and use associative entities in a data model when appropriate. ■
Relate data modeling to process and logic modeling as different ways of describing an information system. ■
Generate at least three alternative design strategies for an information system. ■
Select the best design strategy using both qualitative and quantitative methods. Chapter Preview …
In Chapter 6 you learned how to model and analyze the flow of data (data in motion) between manual or automated steps and how to show data stores (data at rest) in a data-flow diagram. Data-flow diagrams show how, where, and when data are used or changed in an information system, but they do not show the definition, structure, and relationships within the data. Data modeling, the subject of this chapter, develops this missing, and crucial, piece of the description of an information system. Systems analysts perform data modeling during the systems analysis phase, as highlighted in Figure 7-1. Data modeling is typically done at the same time as other requirements structuring steps. Many systems developers believe that a data model is the most important part of the information system requirements statement for four reasons. First, the characteristics of data captured during data modeling are crucial in the design of databases, programs, computer screens, and printed reports. For example, facts such as these—a data element is numeric, a product can be in only one product line at a time, a line item on a customer order can never be moved to another customer order—are all essential in ensuring an information system’s data integrity. FIGURE 7-1
Systems analysts perform data modeling during the systems analysis phase. Data modeling typically occurs in parallel with other requirements structuring steps. Second, data rather than processes are the most complex aspects of many modern information systems. For example, transaction processing systems can have considerable complexity in validating data, reconciling errors, and coordinating the movement of data to various databases. Management information systems (such as sales tracking), decision support systems (such as short-term cash investment), and executive support systems (such as product planning) are data intensive and require extracting data from various data sources. Third, the characteristics about data (such as format and relationships with other data) are rather permanent. In contrast, who receives which data, the format of reports, and what reports are used change constantly over time. A data model...
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