2. Data Models:
Object -Based logical models
Record -Based logical models
Physical Models
The way in which information is subdivided and
managed within a database is referred to as the data
model used by the DBMS. Each DBMS is based on a
particular data model.
Data models can be classified into three major groups.
They are:
3. Data Models:
A. Object-based logical models
These models are used to describe data at the
logical and view levels. The following are the
well known models in this group.
Entity-relationship model
Object-oriented model
Semantic model
Functional model
Contd…
4. B. Record-based logical models
Relational model (e.g., SQL/DS, DB2)
Network model
Hierarchical model (e.g., IMS)
5. Data Models: (Old Classification)
High Level or conceptual data models
Low-level or physical data model
Representational Data Model
6. Entity: An Entity is an object or a thing such as person, place about
which an organization keeps information. Any two objects or things are
distinguishable.
E.g.: Each student is an entity.
Attribute: The describing properties of an entity are called Attributes.
e.g.: For a student entity, name, sex, date of birth are attributes.
Relationship: An association among entities is called a relationship.
The data model that consists of a set of entities and a set of
relationships among those entities is called ER Model.
The set of all entities of the same type is called an entity set and the set
of all relationship of the same type are called a relationship set.
7. Entity-Relationship Model
Example of entity-relationship model
customer account
depositor
social-security customer-street
customer-name
account-number
balance
customer-city
8. The Object-Oriented Model:
The object oriented model is a data model based on a collection of
objects.
Each object has a unique identity. The group of objects containing the
same type of values and the same methods are called classes.
9. The Semantic data model:
These models were based on semantic networks.
Inter dependencies among the entities can be
expressed in this data model.
10. Functional Data Model:
In this model objects, properties of
objects, their relationships are viewed
uniformly and are defined as functions.
11. B)Record Based Logical models:
This model is used to describe data at the logical and view
levels. The database is structured in fixed format records of
different types. Each record type has a fixed number of fields.
And each field is of fixed length.
The following are the three important record based logical
models.
Relational Model
Network Model
Hierarchical Model.
12. Relational Model:
A data model in which both data and their relationships are
represented by means of tables is called Relational Model.
The relation is the only data structure used in this model to
represent both entities and their interrelationships. A relation
is a two dimensional table with a unique name.
Each row of a table is called a tuple and each column of a
table is called an attribute. The set of all possible values in an
attribute is called the domain of the attribute.
13. Relational Model
Example of tabular data in the relational model:
name ssn street city account-number
Johnson 192-83-7465 Alma Palo Alto A-101
Smith 019-28-3746 North Rye A-215
Johnson 192-83-7465 Alma Palo Alto A-201
Jones 321-12-3123 Main Harrison A-217
Smith 019-28-3746 North Rye A-201
account-number balance
A-101 500
A-201 900
A-215 700
A-217 750
14. Network Model:
The network model uses two different
structures. The data are represented by a
collection of records and the relationships
among data are represented by links.
15. Hierarchical Model:
In Hierarchical Model, data are represented by
records and relationships among data are
represented by links. But unlike in Network
model, data are organized in an ordered tree
structure, which is called Hierarchical
structure.
16. C. Physical Data Models:
These models are used to represent data at the
lowest level. Two important physical Data
Models are:
Unifying Model
Frame Memory Model.
17. DBMS Architecture
Architecture is the frame work of the
Database Management System.
Standard database consisting of Conceptual,
external and internal levels.
Conceptual Level-Logical schema of database
External Level-User views of database
Internal Level : Physical views of database
SDLC-A process for
system development
DDLC
18. View of Data
An architecture for a database system
View 1
Physical
level
Logical
level
View 2 View n
…
View level
19. Levels of Abstraction
• Physical level: describes how a record (e.g.
customer) is stored.
• Logical level: describes data stored in database,
and the relationships among the data.
type customer = record
name: string;
street: string;
city: integer;
end;
• View level: application programs hide details of
data types. Views can also hide information (e.g.
salary) for security purposes.
20.
21. DBMS Architecture
Process Manager
Admission Control
Connection Mgr
Query Processor
Parser
Query Rewrite
Optimizer
Executor
Storage Manager
Access Methods
Lock Manager
Buffer Manager
Log Manager
Shared Utilities
Memory Mgr
Disk Space Mgr
Replication Services
Admin Utilities
21
22.
23. Instances and Schemas
• Similar to types and variables in programming
languages
• Schema – the logical structure of the database
(e.g., set of customers and accounts and the
relationship between them)
• Instance – the actual content of the database
at a particular point in time
24. Data Independence
• Ability to modify a schema definition in one
level without affecting a schema definition in
the other levels.
• The interfaces between the various levels and
components should be well defined so that
changes in some parts do not seriously
influence others.
• Two levels of data independence
– Physical data independence
– Logical data independence
26. Backup, restore or delete a single data set separately by
copying or removing the files for its environment.
Balance the load between machines by moving the files for a
single data set from one machine to another.
Improve I/O performance by placing each data set on a
separate physical disk.
Delete individual data sets very efficiently by removing the
environment's log files. This is much more efficient than deleting
individual database records and is also move efficient than
removing databases, and so can be a real benefit if you are
managing large temporary data sets that must be frequently
deleted.
Database environment
