This chapter discusses the concepts of the relational data model. We learn how to convert an ER diagram into a relational schema diagram.

1. Learning Resource
On
Database Management Systems
Chapter-5
The Relational Data Model
Prepared By:
Kunal Anand, Asst. Professor
SCE, KIIT, DU, Bhubaneswar-24

2. Chapter Outcome:
• After the completion of this chapter, the students
will be able to:
– Define different relational model concepts
– Explain several keys in relational model
– Describe constraints in relational model
– Identify the characteristics of relational databases
– Convert ER model into relational model
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3. Organization of this Chapter:
• Introduction
• Relational Model Concepts
• Keys in Relational Model
• Relational Model Constraints
• Characteristics of Relational Model
• Relational Database and its Schema
• Logical Database Design: ER to Relational
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4. Introduction
• The relational data model was introduced by Ted Codd of IBM
Research in 1970.
• The relational data model enables even the novice users to
understand the database, and it permits the use of simple, high
level language to query the data.
• It became very popular in short span of time due to its
simplicity and mathematical foundation.
• The first commercial implementation of the relational data
model became available in early 80s, since then the model has
been implemented in a large number of open source systems.
• Current popular commercial RDBMSs include DB2 from
IBM, Oracle from oracle, Sybase DBMS (now SAP), and SQL
server and Microsoft Access from Microsoft.
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5. Relational Model Concepts
• The main construct to represent the data in the relational
model is a “Relation” which is used to refer to a table.
• In a relation or table, a column header is called an
“attribute”, and a row is called a “tuple”.
• Mathematically, a tuple is a sequence of values. A relationship
between n values can be represented by n-tuples i.e. a tuple of
n values, which corresponds to a row in the table.
• A relational schema contains the name of the relation and
name of all columns or attributes.
• In a relational schema, a domain is referred to by the domain
name and has a set of associated values.
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6. contd..
• In the relational database system, the relational instance is
represented by a finite set of tuples. Relational instances do not
have duplicate tuples.
• The degree is the number of attributes/columns in a table,
whereas cardinality is the number of tuples/rows in a table.
• In a relation, each row has one or more attributes that can
identify the row in the relation uniquely. This is known as
relational key.
• NULL Values: The value which is not known or unavailable is
called NULL value. It is represented by blank space.
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7. contd..
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8. The Instructor Relation
• The instructor relation is represented using the instructor
table, which stores information about the instructors.
• The instructor table has 4 column headers: ID, name,
dept_name, and salary i.e. the table has 4 attributes.
• Each row or tuple of this table records information about an
instructor, consisting of the fields specified in the table.
– t1 = <10101, Srinivasan, Comp. Sci., 65000>
• The relational schema for the instructor table can be
Instructor(ID char, name varchar2, dept_name
varchar2, salary integer)
• The instructor table has an attribute “ID” that can be
considered as relational key as it uniquely identifies each
tuple of the table.
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9. Keys in Relational Model
• Keys play an important role in the relational database.
• A key is a property of the relation, rather than of the tuple.
• It is used to uniquely identify any record or row of data from
the table. It is also used to establish and identify relationships
between tables.
• Usually, following types of keys exist in relational model
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10. Types of Key
• A super key is a set of one or more attributes that allows us to
uniquely identify a tuple in a relation i.e. table.
• A candidate key is a minimal set of super key that cannot have
any columns removed from it without losing the unique
identification property. This property is sometimes known as
minimality or (better) irreducibility.
• All candidate key is super key but the reverse is not true.
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11. contd..
• Primary key is a candidate key that is chosen by the database
designer as the principal means of identifying tuples in a table.
– The primary key should be chosen such that its attributes
are never or very rarely changed.
• The remaining candidate keys, except the primary key, are
called as Alternate keys.
• If none of the columns is a candidate for the primary key in a
table, sometimes database designers use an extra column as a
primary key instead of using a composite key. Such key is
known as the Surrogate key.
• Foreign key is the set of attributes that is used to refer to
another entity set having the primary key.
– In ER diagram, foreign key can not be represented. Foreign
Key is specifically for relational model.
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12. An Example
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13. contd..
• Super Key (SK)
– For Example, the STUDENT table has a super key, SK as
(STUD_NO, STUD_PHONE)
• Candidate Key (CK)
– For Example, STUD_NO, and STUD_PHONE in
STUDENT relation.
– There can be more than one candidate key in a relation. For
Example, STUD_NO as well as STUD_PHONE both are
candidate keys for relation STUDENT.
– The candidate key can be simple (having only one
attribute) or composite as well.
• For Example, {STUD_NO, COURSE_NO} is a composite
candidate key for relation STUDENT_COURSE.
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14. contd..
• Primary Key
– For Example, STUD_NO as well as STUD_PHONE both
are candidate keys for relation STUDENT but STUD_NO
can be chosen as primary key. Why not
STUD_PHONE???
• Alternate Key
– For Example, STUD_NO as well as STUD_PHONE both
are candidate keys for relation STUDENT and STUD_NO
is selected as primary key. So, STUD_PHONE will be
alternate key.
• Foreign Key
– For Example, STUD_NO in STUDENT_COURSE is a
foreign key to STUD_NO in STUDENT relation.
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15. Relational Model Constraints
• Operational Constraints are enforced in the database by the
business rules or real world limitations.
• Relational Data Integrity: Candidate key is an attribute or set
of attributes that can uniquely identify a row or tuple in a table.
– Let R be the relation with attributes a1, a2 ... an . The set of
attributes of R is said to be a candidate key of R iff the
following two properties holds:
• Uniqueness: At any given time, no two distinct tuples or
rows of R have the same value for ai , the same value for
aj ...an
• Minimality: No proper subset of the set (ai , aj ... an ) has
the uniqueness property
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16. Integrity Constraints
• Integrity constraints are a set of rules that is used to maintain
the quality of information.
• Integrity constraints ensure that the data insertion, updation,
and other processes are performed in such a way that data
integrity is not affected.
• Thus, integrity constraint is used to guard against accidental
damage to the database.
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17. contd..
• Domain Constraint
– They can be defined as the definition of a valid set of
values for an attribute.
– The data type of domain includes string, character, integer,
time, date, currency, etc. The value of the attribute must be
available in the corresponding domain.
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18. contd..
• Entity Integrity Constraint
– The entity integrity constraint states that primary key value
can't be null. A table can contain a null value other than the
primary key field.
– This is because the primary key value is used to identify
individual rows in relation and if the primary key has a null
value, then we can't identify those rows.
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19. contd..
• Referential Integrity Constraint: A referential integrity
constraint is specified between two tables.
– In the Referential integrity constraints, if a foreign key in Table 1 refers
to the Primary Key of Table 2, then every value of the Foreign Key in
Table 1 must be null or be available in Table 2.
– A foreign key which references its own relation is known as recursive
foreign key.
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20. contd..
• Key constraints
– Keys are the entity set that is used to identify an entity
within its entity set uniquely.
– An entity set can have multiple keys, but out of which one
key will be the primary key. A primary key can contain a
unique and null value in the relational table.
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21. Database Languages
• Data Definition Language (DDL)
– DDL is used to define the conceptual schema.
– The output of the DDL is placed in the Data Dictionary that
contains the metadata(data about data).
– The data dictionary is considered to be a special type of
table, which can only be accessed and updated by the
database system itself.
– The database system consults the data dictionary, before
querying or modifying the actual data, for the validation
purpose.
– Commands: CREATE, ALTER, DROP, RENAME &
TRUNCATE
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22. contd..
• DML (Data Manipulation Language)
– DML is used to manipulate data in the database.
– A query is a statement in the DML that requests the
retrieval of data from the database.
– Commands: SELECT, INSERT, UPDATE & DELETE
• DCL (Data Control Languages)
•DCL allows in changing the permissions on database
structures
•Commands: GRANT & REVOKE
• TCL (Transaction Control Language)
•TCL allows permanently recording the changes made to the
rows stored in a table or undoing such changes
•Commands: COMMIT, ROLLBACK & SAVEPOINT
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23. Characteristics of Relational Database
• Relational database consists of multiple relations or tables.
• The information about an enterprise is broken up into parts,
with each relation storing one part of the information
• The characteristics of relational database is expressed by
Codd in the form of a set of rules that is widely known as
CODD's Rules.
• CODD's Rules
– Rule 0: A relational system should be able to manage
databases, entirely through its relational capabilities.
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24. contd..
– Rule 1: Information representation
• The entire information is explicitly and logically
represented by the data values of the tables in the
relational data model.
– Rule 2: Guaranteed access
• In relational model, the interaction of each row and
column will have one and only one value of data (or
NULL value).
• Each value of data must be addressable via the
combination of a table name, primary key value and the
column name.
– Rule 3: Systematic treatment of NULL values
• NULL values are supported in fully relational DBMS
for to represent missing information and inapplicable
information in a systematic way independent of data
type.
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25. contd..
– Rule 4: Database description rule
• The database description is represented at the logical level
in the same way as ordinary data, so that authorized users
can apply the same relational language to its interrogation
as they apply to the regular data.
• This means, the RDBMS must have a data dictionary.
• Rule 5: Comprehensive data sub-language
– The RDBMS should have its own extension of SQL.
– The SQL should support Data Definition, View Definition,
Data Manipulation, Integrity Constraint, and Authorization.
• Rule 6: Views updation
– All views that are theoretically updatable are also updatable by
the system. Similarly, the views which are theoretically non-
updatable are also non-updatable by the database system.
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26. contd..
• Rule 7: High-level update, insert, deletes
– A RDBMS should not only support retrieval of data as
relational sets, but also insertion, updation and deletion of
data as a relational set.
• Rule 8: Physical data independence
– Application programs and terminal activities are not
disturbed if any changes are made either to storage
representations or access methods.
• Rule 9: Logical data independence
– User programs and the user should not be aware of any
changes to the structure of the tables such as the addition of
extra columns.
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27. contd..
• Rule 10: Distribution independence
– RDBMS has distribution independence. The RDBMS may
spread across more than one system and across several
networks. However to the end-user, the tables should appear
no different to those that are local.
• Rule 11: Integrity Rules
– Integrity rules must be supported by the database and the
constraints must be stored within the catalogue, separate
from the application.
• Rule 12: Data integrity cannot be subverted
– If a relational system has a low-level language, that low
level cannot be used to subvert or bypass the integrity
rules.
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28. Relational Database and its Schema
• Relational database consists of multiple relations or tables.
• The information about an enterprise is broken up into parts,
with each relation storing one part of the information.
• A relational schema contains the name of the relation and
name of all columns or attributes.
• For the complete database, it is known as relational database
schema that is actually a collection of different schemas that
refers to different relations or tables available in the database.
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29. contd..
• For example: A UNIVERSITY database can have a relational
database schema as below:
– student(ID, name, dept_name, tot_cred)
– advisor(s_id,i_id)
– teaches(ID, course_ID, sec_ID, semester, year)
– takes(ID, course_ID,sec_ID,semester, year, grade)
– section(course_ID, sec_ID, semester, year, building, room_no,
time_slot_id)
– timeslot(time_slot_id, day, start_time, end_time)
– prereq(course_ID, prereq_ID)
– classroom(building, room_number,capacity)
– department(dept_name, building, budget)
– instructor(ID, name, dept_name, salary)
– course(course_ID, title,dept_name,credits)
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30. Relational Schema Diagram
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31. Conversion of ER Diagram to Relational
Schema
• A database that conforms to an ER diagram can be represented
by a collection of relational schemas.
• Both, the ER model and Relational data model are abstract i.e.
logical representations of real-world enterprises.
• Converting Strong entity types
 Each entity type becomes a table
 Each single-valued attribute becomes a column
 Derived attributes are ignored
 Composite attributes are represented by components
 Multi-valued attributes are represented by a separate table
 The key attribute of the entity type becomes the primary
key of the table
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32. Converting Entity Types
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33. Converting weak entity types
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34. Converting Relationships
• The way relationships are represented depends on the
cardinality and the degree of the relationship.
• The possible cardinalities are:
1:1, 1:N, N:1, M:N
• The degrees are:
Unary
Binary
Ternary …
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35. Binary 1:1
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36. contd..
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37. Binary 1:N / N:1
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38. Binary M:N
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39. Unary 1:1
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40. Unary 1:N
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41. Unary M:N
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42. Ternary relationship
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43. Case Study: Company Database
• Problem Statement: The COMPANY database problem has
been discussed in Ch-3: Data Modeling using ER Model.
• The ER details of the Company Database is as below:
– EMPLOYEE (Entity Type)
• Attribute: Emp_name, Emp_ID, SSN, age, address,
gender, salary, DOB, and supervisor
– DEPARTMENT (Entity Type)
• Attribute: Name, Number, Location, Manager, and
Manager_start_date
– PROJECT (Entity Type)
• Attribute: Name, Number, Location, Controlling_dept
– DEPENDENT (Entity Type)
• Attribute: Name, gender, DOB, Employee,
Relationship
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44. contd..
– MANAGES:
• Binary and 1:1 Cardinality
• Entity types: EMPLOYEE and DEPARTMENT
• Participation: EMPLOYEE (Partial); DEPARTMENT
(Total)
– WORKS_FOR:
• Binary and N:1 Cardinality
• Entity types: EMPLOYEE and DEPARTMENT
– CONTROLS:
• Binary and 1:N Cardinality
• Entity types: DEPARTMENT and PROJECT
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45. contd..
– SUPERVISION:
• Unary and 1:N Cardinality
• Entity types: EMPLOYEE
– WORKS_ON:
• Binary and M:N Cardinality
• Entity types: EMPLOYEE and PROJECT
– DEPENDENTS_OF:
• Binary and 1:N Cardinality
• Entity types: EMPLOYEE and DEPENDENT
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46. Case Study: Relational Schema
• Employee(Emp_name, Emp_ID, SSN, address, gender, salary,
DOB, supervisor, Dept_ID)
• Department(Name, Dept_ID, Location, Manager, and
Manager_start_date)
• Project(Name, Proj_ID, Location, Controlling_dept)
• Dependent(Name, gender, DOB, Relationship, Emp_ID)
• Works_On(Emp_ID, Proj_ID, no_of_hrs, supervisor)
Note: Attributes in “Red” are the FOREIGN KEY in that table
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47. Relational Schema Diagram
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48. Representation of Generalization/Specialization
• In case of generalization/specialization-related ER diagram,
one schema will be constructed for the generalized entity set
and the schemas for each of the specialized entity sets
• Person = (person_id, name, address)
• Employee = (emp_id, salary)
• Customer = (cust_id, credit_rating)
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49. contd..
• When the generalization/specialization is a disjointness case,
the schemas are constructed only for the specialized entity sets
– Employee = (employee_id, name, address, salary)
– Customer = (customer_id, name, address, credit_rating)
• Representation of Aggregation
– To represent aggregation, create a schema containing the
primary key of the aggregated relationship, primary key of
the associated entity set and descriptive attributes (if any)
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50. contd..
•Employee = (eid, name, address)
•Branch = (bid, bname, asset)
•Job = (jobid, position, responsibility)
• Works_on = (eid, bid, jobid)
• Manager = (mid, mgrname)
• Manages = (eid, bid, jobid, mid)
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