Welcome: To the fifth learning sequence
“ Data Models “
Recap : In the previous learning sequence, we
discussed The Database concepts.
Present learning: We shall explore the following
topic:
- The most common types of data models.
Data Models
The goal of the data model is to make sure
that the all data objects required by the
database are completely and accurately
represented.
Data Models Importance
•Data modeling is probably the most
intensive and time consuming part of the
development process
•Building a database without a model is like
building a house without plans
Data Models
•Because the data model uses easily
understood notations and natural language it
can be reviewed and verified by end-users
•Detailed enough to be used by the database
developers to use as a blueprint for building
the physical database
Data Models
•The information contained in the data model
will be used to define the relational tables
primary and foreign keys stored procedures
and triggers
•A poorly designed database will require
more time in the long-term .
Data Models
Model : as description or analogy used to
visualized something that be directly observed.
Data model: a collection of concepts that can be
used to describe the structure of a database
provides the necessary means to achieve this
abstraction, by structure of a database , we mean
the data types , relationships , and constraints that
should hold for the data . Most data models also
include a set of basic operations for specifying
retrievals and updates on the database .
Data Models
Some types of data model :
Hierarchical Model *
Network Models *
Relational Models *
* Object-Oriented Model
* Object-Relational Model
* NoSQL Model
* XML Model
Data Models
The hierarchical data models organizes
data in a tree structure .
There is a hierarchy of parent and child data
segments, this structure implies that a record
can have repeating information, generally in
the child data segments, data in a series of
records, which have a set of field values
attached to it .
Data Models
Network data model :
The popularity of the network data model
coincided with the popularity of the
hierarchical data model.
Some data were more naturally modeled
with more than one parent per child, so the
network model permitted the modeling of
many -to-many relationships in data .
Data Models
Relational data model :
A relational database allows the definition of
data structures, storage and retrieval
operations and integrity constraints, in such
a database the data and relations between
them are organized in tables. A table is a
collection of records and each record in a
table contains the same fields.
Data Models
Object-Oriented data Model :
Object DBMSs add database functionality to
object programming languages, they bring
much more than persistent storage of
programming language objects.
Object DBMSs extend the semantics of the
C++ , smalltalk and java object programming
languages to provide full-featured database
programming capability, while retaining
native language compatibility .
Data Models
The American National Standards Institute /
Standard Planning And Requirement
Committee (ANSI / SPARC) define 3
degrees of abstraction as illustrated in Figure
1.
Data Models
The architecture is divided in to three general levels:
1- External levels
This level is concerned with the way in which the data is
viewed by individual users i.e (logical storage).
2- Internal levels
This level is concerned with the way in which the data is
actually stored i.e (physical storage).
3- Conceptual level
It is the level of indirection between the other two levels
(External & Internal levels).
ANSI/SPARC
ANSI/SPARC
External Level
• Represents the user’s view of the database
– Consists a different view of the database for each user.
• Describes the part of the database that is relevant to each user
– For example, in large organizations, the users in each
department will require different user view (interface). Details
needed by Finance department differs than needed by HR.
• Views may provide different representations of the same data
– Date may be formatted (day/month/year) or (year/month/day),
according to user preference.
• Some views might include derived or calculated data
– Date of birth is stored, but in a user view the Age is calculated.
ANSI/SPARC
Conceptual Level
• It describes what data is stored in the database and the
relationships among the data.
• It is a complete view of the data requirements of the organization
that is independent of any storage consideration.
• The conceptual level represents:
– All entities ,their attributes, and their relationships.
– The constraints on the data.
– Security and integrity information.
• The conceptual level supports each external view, so that any data
available to the user must be contained or derived from the
conceptual level.
• these models, sometimes called domain models , are typically used
to identify and document business (domain) concepts with project
stakeholders(users)..
ANSI/SPARC
Internal Level
• It covers the physical representation of the database on the
computer.
• It describes how the data is stored in the database in terms of
particular data structures and file organizations.
• The internal level is concerned with:
– Allocating storage space for data and indexes.
– Describing the forms that records will take when stored.
– Record placement. Assembling records into files.
– Data compression and encryption techniques.
• The internal level interfaces with the OS to place data on the storage
devices, build the indexes, retrieve the data, etc.
• It deals with the mechanics of physically storing data on a device
such as a disk.
ANSI/SPARC
Database Schemas
• The overall description of a database is called the database
schema.
• There are three different types of schema corresponding to the three
levels in the -ANSI/SPARC architecture.
– External schema (many schemas may exist)
– Conceptual schema (only one per database)
– Internal schema (only one per database)
• The DBMS is responsible for mapping between the three types of
schema (i.e. how they actually correspond with each other).
• Each external schema is related to the conceptual schema
(external/conceptual mapping).
• A conceptual/internal mapping relates the conceptual schema to the
internal schema.
ANSI/SPARC
User A1
Language
Workspace
User A2
User B1
Language
Workspace
Data Base Administrator (DBA)
*
Language
Workspace
Ext. External
Schema A Model A
Ext./Con.
Mapping A
Conc.
Schema
User B2
Language
Workspace
User B3
Language
Workspace
.
*
Ext. External
Schema B Model B
Conceptual
Model
Ext./Con.
Mapping B
DBMS
*
User interface
Inte.
Schema
Stored database (Internal Model)
Fig.1: An Architecture for Database System
ANSI/SPARC
ANSI/SPARC
• The main objective of the three-level architecture is to
“separate the users’ view(s) of the database from the
way it is physically represented”, which will result:
– Allowing independent customized user views.
– Hiding the physical storage details from the users.
– The DBA can change the database storage structures
without affecting the users’ views.
– Conceptual structure can be changed without
affecting the users’ views.
ANSI/SPARC
Database languages
-- Each user of a database system has a language of his or her
disposal. What is important about the used language is that it will
include a Data Sub-Language (DSL), which is that subset of the
language concerned with database objects and operations.
-- Any given DSL is really a combination of:
1- Data Definition Language DDL, which defines the database objects
(creating tables, fields, primary and foreign keys)
2- Data Manipulation Language DML, which supports the
manipulation or processing of those objects. (Insert, update, delete
data)
3- Structured Query Language SQL, which supports displaying and
retrieving database objects.
- Each user provided with a workspace, which acts as receiving or
transmitting area between the user and the database.
Data Models
Categories of data models :
Conceptual data models : these models,
sometimes called domain models , are
typically used to identify and document
business (domain) concepts with project
stakeholders. Conceptual data models are
often created as the precursor to logical data
models (LDMs) or as alternatives to LDMs .
Data Models
Logical data models (LDMs) : logical data
models are used to further explore the
domain concepts, and their relationships and
relationship cardinalities. This could be done
for the scope of a single project or for your
entire enterprise. Logical data models depict
the logical entity types, typically referred to
simply as entity types, the data attributes
describing those entities, DDL can be
generated at this level .
Data Models
Physical data models (PDMs) : physical data
models are used to design the internal
schema of a database, depicting the data
tables (derived from the logical data entities),
the data columns of those tables (derived
from the entity attributes), and the
relationships between the tables derived
from the entity relationships .
Data Models
Major event in data model include :
- Identifying the data and associated processes.
- Defining the data (such as data types , sizes , and
defaults).
- Specifying data storage requirements.
- Defining the data management processes (such
as security reviews and backups.
- Ensuring data integrity (by using business rules
and validation checks).
Data Models
Summary: In this learning sequence, we
discussed the best common types of data
models.
Data Models
end