The document discusses various data models used in database management systems (DBMS), including object-based, record-based logical models, and their specific types like relational, hierarchical, and network models. It highlights the advantages and disadvantages of each model, explaining concepts such as relationships, integrity rules, and keys, particularly focusing on the relational model developed by E.F. Codd. Additionally, it touches upon query languages used to interact with databases, distinguishing between procedural and non-procedural types.

1. Data models

2. • Object based logical model
– E-R Model
– Object Oriented Model
• Record Based Logical Model
– Network Model
– Hierarchical Model
– Relational Model

3. RDBMS - Relational Database
Management System)
• A database based on the relational model developed
by E.F. Codd in 1960.
• A relational database allows the definition of data
structures, storage and retrieval operations and
integrity constraints.
• Properties of Relational Tables:
– Values Are Atomic
– Each Row is Unique
– Column Values Are of the Same Kind
– The Sequence of Columns is Insignificant
– The Sequence of Rows is Insignificant
– Each Column Has a Unique Name
– All operations are performed on an entire relation and
result is an entire relation a concept known as closure

4. Network model
• Record based logical model
• All data is store in collection of records. And
relationship with links.
• Organized as arbitrary graph.
• So, the network model permitted the modeling of
many-to-many relationships in data. Which is not
allowed in hierarchical model.
• Thus, the complete network of relationships is
represented by several pairwise sets; in each set some
(one) record type is owner (at the tail of the network
arrow) and one or more record types are members (at
the head of the relationship arrow).

5. • The main difference of the network model
from the hierarchical model, is its ability to
handle many to many (N:N) relations.
• No insertion, Deletion, updation anomaly.
• But required large number of pointers.
• Making structural changes is very difficult.
• Complex retrieval.

6. Hierarchical Data Model
• Record based logical model
• The hierarchical data model organizes data in a tree
structure. Rather than the arbitrary graph.
• It also forms a forest.
• All data is store in record or node. And relationship with
links. And fields with branches.
• Root of the tree is dummy node.
• 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
• This restricts a child segment to having only one parent
segment.
• Hierarchical DBMSs were popular from the late 1960s, with
the introduction of IBM's Information Management System
(IMS) DBMS, through the 1970s.

7. Hierarchical Data Model
• One of the oldest database models from 1950.
• Information Management System (IMS) was
developed jointly by north American Rockwell
company and IBM.
• Adv : simplicity, security, data integrity, efficiency.
• Disadv : Implementation complexity,
management Problem, Lack of structural
Independence, program complexity, operational
anomalies, Implementation limitation.

8. Object-Oriented Model
• The object-oriented model can be seen as extending the E-
R model with notions of encapsulation, methods
(functions), and object identity.
• Model database as a Collection of objects.
• Each object has a Data (variable) and methods (functions)
• Same data variables and same type of functions will be
grouped together to form a class.
• Object DBMSs add database functionality to object
programming languages.
Object Name
Variables
Functions

9. • Postgres
• Starburst
• GemStone
• ONTOS
• ObjectStore
– Application
• CAD/CAM .
• Multimedia

10. Other models
• Object/Relational Model
• Semistructured Model
• Associative Model
• Entity-Attribute-Value (EAV) data model
• Context Model
• http://unixspace.com/context/databases.html

11. Relational data Model
• Use collection of tables to represent both data
and relationship among data.
– Table known as Relation
– Each table will have unique name
– Each column will have unique name
– Attributes -> Columns
– Possible set of Values in column is domain.
– Record of one entity -> rows -> tuple.
– Degree -> number of attributes in relation.
– Cardinality -> number of tuples in relation. Number of
values.
• Domain is a set of atomic values.
• Atomic means Each value indivisible.

12. • All operations are performed on an entire
relation and result is an entire relation a concept
is known as closure.
• Keys
– Primary
– Candidate
– Alternative
– Super
• Integrity Rules
– 1. Entity integrity rule: if Column A is a primary key it
cannot accept null value and all values will be unique
– 2. Referential integrity rule : cannot add a value in
foreign key column if that value doesnot exist in
primary key column.

13. Example of a Relation
account

14. Attribute Types
• Each attribute of a relation has a name
• The set of allowed values for each attribute is called the
domain of the attribute.
• Attribute values are (normally) required to be atomic, that is,
indivisible
– E.g. multivalued attribute values are not atomic
– E.g. composite attribute values are not atomic
• The special value null is a member of every domain
• Domain is a set of atomic values
• Atomic means Each value indivisible.

15. Relation Instance
• The current values (relation instance) of a relation are
specified by a table
• An element t of r is a tuple, represented by a row in a table
Jones
Smith
Curry
Lindsay
customer-name
Main
North
North
Park
customer-street
Harrison
Rye
Rye
Pittsfield
customer-city
customer
attributes
(or columns)
tuples
(or rows

16. Relations are Unordered
• Order of tuples is irrelevant (tuples may be stored in
an arbitrary order)
• E.g. account relation with unordered tuples

17. Database
• A database consists of multiple relations
• Information about an enterprise is broken up into parts, with each relation
storing one part of the information
E.g.: account : stores information about accounts
depositor : stores information about which customer owns
which account
customer : stores information about customers
• Storing all information as a single relation such as
bank (account-number, balance, customer-name, ..)
results in
– repetition of information (e.g. two customers own an account)
– the need for null values (e.g. represent a customer without an account)

18. E-R Diagram for the Banking Enterprise

19. The customer Relation

20. The depositor Relation

21. Keys
• Let K  R
• K is a superkey of R if values for K are sufficient to identify a
unique tuple of each possible relation r(R)
– by “possible r” we mean a relation r that could exist in the
enterprise we are modeling.
– Example: {customer-name, customer-street} and
{customer-name}
are both superkeys of Customer, if no two customers can
possibly have the same name.
• K is a candidate key if K is minimal
Example: {customer-name} is a candidate key for Customer

22. Determining Keys from E-R Sets
• Strong entity set. The primary key of the entity set becomes
the primary key of the relation.
• Weak entity set. The primary key of the relation consists of
the union of the primary key of the strong entity set and the
discriminator of the weak entity set.
• Relationship set. The union of the primary keys of the related
entity sets becomes a super key of the relation.
– For binary many-to-one relationship sets, the primary key
of the “many” entity set becomes the relation’s primary
key.
– For one-to-one relationship sets, the relation’s primary key
can be that of either entity set.
– For many-to-many relationship sets, the union of the
primary keys becomes the relation’s primary key

23. Query Languages
• Language in which user requests information from the
database. And interface b/w user and Database.
• Categories of languages
–Procedural : user has to specify what data
to get and also how to get those data.
(Procedure to obtain that data)
• Relational Algebra
–non-procedural : : user has to specify what
data to get without specifying how to get
those data.
• Relational Calculus
– Tuple Relational calculus
– Domain Relational calculus

24. Relational Algebra
• Procedural Query Languages.
• Operation to be performed on exciting relation to derive the result
relation.
• Unary operation.
– Select
– Project
– Rename
• Binary operation.
– Cartesian product
– Union
– Set difference
• Additional Operation
– Intersection
– Join
– Division
– Assignment