What Is a Data Dictionary?

The data dictionary refers to the definition and description of data items, data structures, data flows, data storage, processing logic, etc. The purpose of the data dictionary is to make detailed descriptions of each element in the data flow diagram. Modeling project. In short, a data dictionary is a collection of information describing data, a collection of definitions for all data elements used in the system.

Data Dictionary

The most important role of the data dictionary is as a tool in the analysis phase. The most important use of any dictionary is for people to query the interpretation of unknown entries, in

The data dictionary is built during the requirements analysis phase.

The data dictionary is a reserved space, a database which is used

The composition of the data dictionary:

1,

Description of each part of the data dictionary

Data item : Explanation of data items in the data structure of the data block in the data flow diagram

Data items are indivisible data units. The description of a data item usually includes the following:

Data item description = {data item name, data item meaning description, alias, data type, length,

Value range, value meaning, logical relationship with other data items}

The "value range" and "logical relationship with other data items" define the integrity constraints of the data and are the basis for designing the data inspection function.

Several data items can form a data structure.

Data structure : Explanation of the data structure of the data block in the data flow diagram

The data structure reflects the combined relationship between the data. A data structure can be composed of several data items, it can also be composed of several data structures, or it can be composed of several data items and data structures. The description of the data structure usually includes the following:

Data structure description = {data structure name, meaning description, composition: {data item or data structure}}

Data flow : explanation of the flow lines in the data flow diagram

A data stream is a path through which data structures are transmitted within the system. The description of the data flow usually includes the following:

Data flow description = {data flow name, description, data flow source, data flow destination,
Composition: {data structure}, average traffic, peak traffic}

The "data stream source" indicates which process the data stream comes from, that is, the source of the data. "Data flow destination" is to indicate which process the data flow is going to, that is, where the data is going. "Average traffic" refers to the number of transmissions per unit time (daily, weekly, monthly, etc.). "Peak traffic" refers to data traffic during peak times.

Data storage : the storage characteristics of data blocks in the data flow diagram

Data storage is where the data structure stays or is stored, and it is also one of the sources and destinations of the data flow. The description of the data store usually includes the following:

Data storage description = {data storage name, description, number, incoming data stream, outgoing data stream,
Composition: {data structure}, data volume, access method}

The "data amount" refers to how much data is accessed at a time, and how many times are accessed every day (or every hour, week, etc.). The "access method" includes batch processing or online processing; retrieval or update; sequential retrieval or random retrieval.

In addition, the "incoming data stream" should indicate its source, and the "outgoing data stream" should indicate its destination.

Processing process : description of function blocks in data flow diagram

The data dictionary only needs descriptive information describing the process, and usually includes the following:

Process description = {process name, description, input: {data stream}, output: {data stream},
Processing: (brief description)}

The "brief description" mainly describes the function and processing requirements of the process. Function refers to what the processing process is used to do (not how to do); processing requirements include processing frequency requirements, such as how many transactions are processed per unit time, how much data volume, response time requirements, etc. These processing requirements are the physical design behind Input and performance evaluation criteria.