The term data modeling describes two different things: data structure and the way data are organized using database management system.

1. DATA Models
THURSDAY, OCTOBER 13, 2016

2. Learning Outcomes:
At the end of the lesson, I should be able to:
a) Explain the term data modeling
b) State the types of data models
c) Significance of data models

3. Data Modeling
This is the process of structuring and organizing
data.
The term data modeling describes two different
things: data structure and the way data are
organized using database management system.

4. Data structure
A data model describes the structure of the data within a given domain
and, by implication, the underlying structure of that domain itself.

5. Data organization
This is the organization of data using database management systems or
other data management technology.

6. Approach in Data Modeling
Data modeling approach includes the following:
1. conceptual data modeling
2. logical data modeling
3.physical data modeling

7. Three perspectives - Approach
A data model instance may be one of three kinds according to ANSI in 1975:
Conceptual data model : describes the semantics of a domain, being the
scope of the model. For example, it may be a model of the interest area of
an organization or industry. This consists of entity classes, representing
kinds of things of significance in the domain, and relationship assertions
about associations between pairs of entity classes. A conceptual schema
specifies the kinds of facts or propositions that can be expressed using the
model. In that sense, it defines the allowed expressions in an artificial
'language' with a scope that is limited by the scope of the model.
Logical data model : describes the semantics, as represented by a
particular data manipulation technology. This consists of descriptions of
tables and columns, object oriented classes, and XML tags, among other
things.
Physical data model : describes the physical means by which data are
stored. This is concerned with partitions, CPUs, tablespaces, and the like.

8. CONCEPTUAL DATA MODELING
This identifies the highest-level relationship between different entities.
Conceptual is the first step in organizing the data requirements.

9. Logical Data Modeling
This illustrates specific entities, attributes and relationships involved in a
business function. It consists of tables, columns, object-oriented classes,
and XML tags.

10. Physical Data Modeling
This represents an application (such as SQL) and database-specific
implementation of a logical data model and describes the physical
means used to store data.

11. Types of Data Modeling
1. Flat model
2. Hierarchical model
3. Network model
4. Relational model
5. Object-Oriented Model
6. Star schema

12. FLAT MODEL DATABASE
The flat (or table) model consists of a single, two-dimensional array of
data elements, where all members are assumed to be of similar values,
all members of a row are assumed to be related to one another.

13. 14.13
HIERARCHICAL DATABASE MODEL
In the hierarchical model, data is organized as an inverted
tree. Each entity has only one parent but can have several
children. At the top of the hierarchy, there is one entity,
which is called the root.
An example of the hierarchical model representing a university

14. 14.14
NETWORK DATABASE MODEL
In the network model, the entities are organized in a graph,
in which some entities can be accessed through several paths
(Figure 14.4).
An example of the network model representing a university

15. 14.15
RELATIONAL DATABASE MODEL
In the relational model, data is organized in two-dimensional
tables called relations. The tables or relations are, however,
related to each other, as we will see shortly.
An example of the relational model representing a university

16. OBJECT ORIENTED DATABASE MODEL
An object database (also object-oriented database management
system, OODBMS) is a database management system in which
information is represented in the form of objects as used in object-
oriented programming. Object databases are different from relational
databases which are table-oriented. Object-relational databases are a
hybrid of both approaches.

17. STAR SCHEMA
This is the simplest style of data mart schema and is the approach most
widely used to develop data warehouses and dimensional data marts.
The star schema consists of one or more fact tables referencing any
number of dimension tables. The star schema is an important special
case of the snowflake schema, and is more effective for handling
simpler queries.

18. Significance of Data models
1. Data models can facilitate interaction among the designer, the
application programmer and the end user.
2. A well- developed data model can even foster improved
understanding of the organization for which the database design is
developed.
3. Data models are a communication tool.
4. Data models help in structuring and organizing data.
5. Data models impose constraints or limitations on the data placed
within the structure.

19. Standard Data Model
A standard data model or industry standard data model (ISDM) is a data
model that is widely applied in some industry, and shared amongst
competitors to some degree. They are often defined by standards
bodies, database vendors or operating system vendors.

20. Examples of standard data
models
1. ISO 10303 CAE Data Exchange Standard - includes its own data
modelling language, EXPRESS
2. ISO 15926 Process Plants including Oil and Gas facilities Life-Cycle
data
3. IDEAS Group Foundation Ontology agreed by defence
departments of Australia, Canada, France, Sweden, UK and USA
4. Common Education Data Standards (CEDS) is a data dictionary
sponsored by the US government that is used widely in the United
States education vertical
5. SIF is an interoperability specification used as a standard data
model in Australia, the UK, and the US.

21. Assignment
Read up the topic: Normal forms and explain the determination of
normal forms.

22. Terminologies
1. Data model
2. Forms
3. Queries
4. Iterations
5. Implicitly
6. Explicitly