Chapter 8 - Analyzing Systems Using Data Dictionaries

Data dictionary entries vary according to

the level of the corresponding data flow diagram

Logical data structures reflect

the mental model of how the users view the system

Data Dictionaries may be used to

1 - Create reports, screens, and forms 2 - Generate computer program source code 3 - Analyze the system design for completion and to detect design flaws 4 - Create XML document structures Create extensible markup language (XML)

Additional elements on these structures include:

1 - Key fields used to locate records 2 - Codes to indicate record status 3 - Codes to identify records when multiple record types exist on a single file 4 - A count of how many entries are actually in a repeating group 5 - Limits on the number of items in a repeating group 6 - A password used by a customer accessing a secure website

Document Type Definition (DTD) is one way to define what is allowed in an XML document, and can include:

1 - The order of the elements 2 - If an XML element is repeatable, and whether there must beat least one of the elements 3 - An optional element 4 - Definition of attributes

An algebraic notation is used to represent the data structure. The symbols used are:

1. Equal sign = meaning consists "of" 2. Plus sign + meaning "and" 3. Braces {} meaning repetitive elements, a repeating element or group of elements 4. Brackets [] for an either/or situation 5. Parentheses () for an optional element

A data repository is a large collection of project information. It includes:

1. Information about system data, including data flows, data stores, record structures, elements, entities, and messages 2. Procedural logic and use cases 3. Screen and report design 4. Relationships between entries 5. Project requirements and deliverable 6. Project management information

The data dictionary may be used for the following reasons:

1. Providing documentation 2. Eliminating redundancy 3. Validating the data flow diagram for completeness and accuracy. 4. Providing a starting point for developing screens and reports. 5. Determining the contents of data that is stored 6. Developing the logic for data flow diagram processes. 7. Creating XML (extensible markup language)

The data dictionary is a reference work of data about data (metadata) compiled by systems analysts to guide them through analysis and design.

It collects, coordinates, and confirms what a specific data term means to different people in the organization.

A data dictionary is

a main method for analyzing the data flows and data stores of data-oriented systems.

Data stores must be defined. They contained

a minimal of all base elements as well as many derived elements

An XML schema is

another more precise way to define the content of an XML document

Data stores may be determined by analyzing data flows. Each data store should

consist of elements on the data flows that are logically related, meaning they describe the same enity.

An XML document may be used to

define, filter, sort and transform data

Each data flow should be defined with

descriptive information and its composite structure (elements)

Data stores definitions contain

descriptive information as well as physical characteristics. The name of the data structure found with the data store should be included.

Data Structures may be

either logical or physical

Data dictionaries are created

in a top-down manner

Physical Data Structures

include elements and information necessary to design and implement the computer system

XML is used to

share data between 2 different computer systems.

To have maximum power, the data dictionary should be

tied into other programs in the system so that when an item is updated or deleted from the data dictionary it is automatically updated or deleted from the database. Otherwise, the data dictionary becomes only a type of historic document of the system.

A starting point in creating data dictionary entries is

to analyze user input and output requirements

Data elements should be defined

with descriptive information, length, and types of data information, validation criteria, and default values