Chapter 8, Analyzing Systems Using Data Dictionaries
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Structural metadata
Structural metadata describes structural and logical structure of digital resources in terms of intellectual boundaries of complex objects. They are utilized in the description of object relationships between the object components. An example of structural metadata is a visual and audio stream in a slide presentation.
Technical metadata
Technical metadata refers to the technical attributes of digital objects including production and creation information on the digital front. This includes the hardware and software used to acquire the digital objects.
Business metadata
This includes details of other information about the data such as keywords relating to meta objects and their notes. Business metadata comprise of the type of information contained in the metadata and is far much different from physical and view metadata that defines what the metadata represent.
Administrative metadata
This includes the administrative elements that aid in the management of resources to include when and how it was created, file type and technical information such as who gain access to it. The two subsets of administrative metadata include rights management metadata and preservation metadata. The latter contains the information needed to archive and preserve a resource.
Descriptive metadata
Describe the features of the resource such and discovery and identification. It includes elements like author, abstract, title and keywords.
Use metadata
This is metadata related to the level and type of use of information resources. Examples are exhibit records, use and user tracking and content re-use and multi-versioning information.
Preservation metadata
Involves the metadata related to the preservation and management of information resources. Examples such as documentation of physical conditions of resources, documentation of actions aimed at preserving physical and digital versions of resources such as refreshing and migration qualify as preservation metadata.
Process metadata
This describes the results of various operations and procedures in a warehouse. The metadata describes how datasets are acquired, processed, stored and retrieved in a warehouse.
The above data dictionary categories can be summarized as data flows, data structures, data elements and data stores.
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Elements are comprised of classes, functions, quantities and units. Base elements comprise of those that are measured by itself while derived elements comprise of those that are calculated from the base.
For instance, the derived class inherits or is derived from the base class. In a more simplistic manner, we consider a “child” and “parent”. The child class derives its attributes from the parent class. Thus, the parent is the base class while the child is the derived class. The variables and methods of the derived class are derived from the base class and any changes done to the parent class will affect the child class.
In terms of units, base units are elements that stand by themselves and include height and length. Derived units, on the other hand, represent the calculated elements such as volume, density and mass. Other examples of derived quantities include kinetic energy, and velocity. Derived quantities are derived according to the corresponding situation. Sometimes, a derived quantity is measured and considered as a base quantity. Examples of such an element include velocity.
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Database management systems are equipped with data dictionaries. The automated data dictionaries can either be elaborate or simple and some automatically catalog data items during programming while others prompt the user with a template to execute it manually. Nonetheless, an understanding of the composition of data dictionary, its conventions and developments designs together with its advantages is essential.
The principle aim of using data dictionaries is to eliminate redundancy and keep data clean. Data dictionary is a reference work of data about the data and is compiled by system analyst to provide a guideline during system analysis and design.
A record about a female sex saved as Female in one record and F in another and O, in a third, record represents unclean data. A data dictionary is meant to define a standard method of representing data such that conflicting records representing the same attribute are eliminated.
In addition to eliminating data redundancy in a database, data dictionary seeks to validate data flow diagrams for completeness and accuracy. Data flow diagrams are important CASE tools used in designing and representing graphical databases and information systems. Understanding the concept of a data dictionary helps facilitate system analyst in conceptualizing the system and describing how it works.
It also provides a starting point for developing screens and reports. Screens and reports are paramount for efficient user interfaces and decision making. A clearly designed screen and report cannot be achieved without invoking data dictionaries.
Other main functions include logic for data flow and diagram processes and determination of contents stored in files. Each data store and data flows are defined and expanded to include specifications of the contents it contains. Logic of each process should be described using data flowing into and out of the process, and it are through data dictionaries that omissions and other design errors are diagnosed and resolved.
Data dictionary collects and coordinates certain specific data terms, and it combines what each and every means to different people in the organization. Their importance cannot be underestimated as they provide the required data redundancy.
Reference
Gary B. Shelly, T. J. (2011). Systems Analysis and Design: Eighth Edition. Cengage Learning .
Philander, L. &. (2008). FCS Systems Analysis & Design L4. Pearson South Africa.
