Data Dictionary

1. Data Dictionary What does “Backordered item” mean? What does “New Customer info.” contain? How does the “account receivable report” look like?

2. Data Dictionary • A list of names used in the system models arranged alphabetically and their meaning and information about them • Types of information provided: • Description of data • If composite data, describe elements • Data representation or type • Creator, creation date, user contacts • Other names for the data (aliases)

3. Data Dictionary • Data dictionary is a metadata • Created in parallel with DFD • Created in top-down approach • Data dictionary should be kept up-to-date with the DFD model

4. Data Dictionary • Advantages: • Documentation for the whole system • Eliminate redundancy in a system which has been created by different people • Identify aliases • Provide a starting point to develop reports and screens • Validates the data flow diagrams for completeness and accuracy • Develop the logic for DFD processes

5. Data flow Description • ID number • Name (as it appears in DFD) • Description • Source • Destination • Type (file, screen, report, form, internal) • Data structure name • How frequently produced • Comments

6. Data Store • ID number • Name • Alias name • Description • Type (manual, computerized) • File format (database, sequential, Indexed) • Maximum/ average number of recording • File name • Data structure

7. Data Structure • For compositional data flow and data store elements • Describe the elements of the data structure using algebraic notation

8. Data Structure • Example: Student record= student ID + Student name+ Address + Date of birth + Gender + Nationality + Telephone number + (mobile telephone number) + { course record} Course record=course name + Course number+ Grade

9. Data Elements • Each element in the data structure should be described • ID number • Name • Alias • Description • Type (Base: entered from user, derived: computed) • Length (in characters) [max or fixed] • Data type (date, numeric, alphanumeric) • Input/Output format • Validation criteria • Default value • Comments

10. Process Data • Process number • Process name • Description • Input • Output • Type (batch, online, manual) • Prewritten code: ready made functional libraries • Process logic • Reference to description tables or structures • Unresolved issues: notes to check with users

11. Describing Processes • Using structured English or pseudo code • Advantages • Understand how the process works and go back to users if not clear • Describe what should happen for designer and programmer • Validate the DFD

12. Describing Processes • Example: Bonus monthly payment (Structured English) For each customer Read Customer record For each item rented between 1/10 to 1/11 add total End for If total is greater than or equal to 50$ then send bonus and thank you letter End if End for

13. Logical and Physical data • Logical data structures is what the user sees • Physical data structure is what the software needs for processing • (e.g. flags, key fields for search, ..)

14. Automatic Data Dictionary • Using CASE tools: • Easy to change and add • Created and integrated with other models • Checks that the DFD is valid • e.g. all data needed for a process is input to it • Data store contains data input/ output to/from it • Derived data should be output from process • Checks data source and destination in DFD • Use data structures to create reports and screens