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Database Management Systems

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Database Management System (CS403)
VU
Lecture No. 16
Reading Material
"Database Systems Principles, Design and Implementation"
Page 209
written by Catherine Ricardo, Maxwell Macmillan.
Overview of Lecture:
Mapping Relationships
o
Binary Relationships
o
Unary Relationships
o
Data Manipulation Languages
o
In the previous lecture we discussed the integrity constraints. How conceptual
database is converted into logical database design, composite and multi-valued
attributes. In this lecture we will discuss different mapping relationships.
Mapping Relationships
We have up till now converted an entity type and its attributes into RDM. Before
establishing any relationship in between different relations, it is must to study the
cardinality and degree of the relationship. There is a difference in between relation
and relationship. Relation is a structure, which is obtained by converting an entity
type in E-R model into a relation, whereas a relationship is in between two relations
of relational data model. Relationships in relational data model are mapped according
to their degree and cardinalities. It means before establishing a relationship there
cardinality and degree is important.
Binary Relationships
Binary relationships are those, which are established between two entity type.
Following are the three types of cardinalities for binary relationships:
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Database Management System (CS403)
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o One to One
o One to Many
o Many to Many
In the following treatment in each of these situations is discussed.
One to Many:
In this type of cardinality one instance of a relation or entity type is mapped with
many instances of second entity type, and inversely one instance of second entity type
is mapped with one instance of first entity type. The participating entity types will be
transformed into relations as has been already discussed. The relationship in this
particular case will be implemented by placing the PK of the entity type (or
corresponding relation) against one side of relationship will be included in the entity
type (or corresponding relation) on the many side of the relationship as foreign key
(FK). By declaring the PK-FK link between the two relations the referential integrity
constraint is implemented automatically, which means that value of foreign key is
either null or matches with its value in the home relation.
For Example, consider the binary relationship given in the figure 1 involving two
entity types PROJET and EMPLOYEE. Now there is a one to many relationships
between these two. On any one project many employees can work and one employee
can work on only one project.
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empNameme
prDuratio
prCost
empSal
prId
empId
np
PROJECT
EMPLOYEE
Fig. 1: A one to many relationship
The two participating entity types are transformed into relations and the relationship is
implemented by including the PK of PROJECT (prId) into the EMPLOYEE as FK.
So the transformation will be:
PROJECT (prId, prDura, prCost)
EMPLOYEE (empId, empName, empSal, prId)
The PK of the PROJECT has been included in EMPLOYEE as FK; both keys do not
need to have same name, but they must have the same domain.
Minimum Cardinality:
This is a very important point, as minimum cardinality on one side needs special
attention. Like in previous example an employee cannot exist if project is not assigned.
So in that case the minimum cardinality has to be one. On the other hand if an
instance of EMPLOYEE can exist with out being linked with an instance of the
PROJECT then the minimum cardinality has to be zero. If the minimum cardinality is
zero, then the FK is defined as normal and it can have the Null value, on the other
hand if it is one then we have to declare the FK attribute(s) as Not Null. The Not Null
constraint makes it a must to enter the value in the attribute(s) whereas the FK
constraint will enforce the value to be a legal one. So you have to see the minimum
cardinality while implementing a one to many relationship.
Many to Many Relationship:
In this type of relationship one instance of first entity can be mapped with many
instances of second entity. Similarly one instance of second entity can be mapped
with many instances of first entity type. In many to many relationship a third table is
created for the relationship, which is also called as associative entity type. Generally,
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the primary keys of the participating entity types are used as primary key of the third
table.
For Example, there are two entity types BOOK and STD (student). Now many
students can borrow a book and similarly many books can be issued to a student, so in
this manner there is a many to many relationship. Now there would be a third relation
as well which will have its primary key after combining primary keys of BOOK and
STD. We have named that as transaction TRANS. Following are the attributes of
these relations: -
o STD (stId, sName, sFname)
o BOOK (bkId, bkTitle, bkAuth)
o TRANS (stId,bkId, isDate,rtDate)
Now here the third relation TRANS has four attributes first two are the primary keys
of two entities whereas the last two are issue date and return date.
One to One Relationship:
This is a special form of one to many relationship, in which one instance of first entity
type is mapped with one instance of second entity type and also the other way round.
In this relationship primary key of one entity type has to be included on other as
foreign key. Normally primary key of compulsory side is included in the optional side.
For example, there are two entities STD and STAPPLE (student application for
scholarship). Now the relationship from STD to STAPPLE is optional whereas
STAPPLE to STD is compulsory. That means every instance of STAPPLE must be
related with one instance of STD, whereas it is not a must for an instance of STD to
be related to an instance of STAPPLE, however, if it is related then it will be related
to one instance of STAPPLE, that is, one student can give just one scholarship
application. This relationship is shown in the figure below:
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scAmount
scId
stName
stId
STD
SCAPPL
Fig. 2: A one to one relationship
While transforming, two relations will be created, one for STD and HOBBY each. For
relationship PK of either one can be included in the other, it will work. But preferably,
we should include the PK of STD in HOBBY as FK with Not Null constraint imposed
on it.
STD (stId, stName)
STAPPLE (scId, scAmount, stId)
The advantage of including the PK of STD in STAPPLE as FK is that any instance of
STAPPLE will definitely have a value in the FK attribute, that is, stId. Whereas if we
do other way round; we include the PK of STAPPLE in STD as FK, then since the
relationship is optional from STD side, the instances of STD may have Null value in
the FK attribute (scId), causing the wastage of storage. More the number records with
Null value more wastage.
Unary Relationship
These are the relationships, which involve a single entity. These are also called
recursive relationships. Unary relationships may have one to one, one to many and
many to many cardinalities. In unary one to one and one to may relationships, the PK
of same entity type is used as foreign key in the same relation and obviously with the
different name since same attribute name cannot be used in the same table. The
example of one to one relationship is shown in the figure below:
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empId
empName
EMPLOYEE (empId, empName, empAdr, mgr)
EMPLOYEE
MANAGES
empAdr
(a)
stId
stName
STUDENT (stId, stName, roommate)
STUDENT
ROOMMATE
(b)
Fig. 3: One to one relationships (a) one to many (b) one to one
and their transformation
In many to many relationships another relation is created with composite key. For
example there is an entity type PART may have many to many recursive relationships,
meaning one part consists of many parts and one part may be used in many parts. So
in this case this is a many to many relationship. The treatment of such a relationship is
shown in the figure below:
partId
partName
PART
MANAGES
PART (partId, partName)
SUB-PART (partId, component)
Fig. 4: Recursive many to many relationship
and transformation
Super / Subtype Relationship:
Separate relations are created for each super type and subtypes. It means if there is
one super type and there are three subtypes, so then four relations are to be created.
After creating these relations then attributes are assigned. Common attributes are
assigned to super type and specialized attributes are assigned to concerned subtypes.
Primary key of super type is included in all relations that work for both link and
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identity. Now to link the super type with concerned subtype there is a requirement of
descriptive attribute, which is called as discriminator. It is used to identify which
subtype is to be linked. For Example there is an entity type EMP which is a super type,
now there are three subtypes, which are salaried, hourly and consultants. So now there
is a requirement of a determinant, which can identify that which subtypes to be
consulted, so with empId a special character can be added which can be used to
identify the concerned subtype.
Summary of Mapping E-R Diagram to Relational DM:
We have up till now studied that how conceptual database design is converted into
logical database. E-R data model is semantically rich and it has number of constructs
for representing the whole system. Conceptual database is free of any data model,
whereas logical database the required data model is chosen; in our case it is relational
data model. First we identified the entity types, weak and strong entity types. Then we
converted those entities into relations. After converting entities into relations then
attributes are identified, different types of attributes are identified. Then relationships
were made, in which cardinality and degree was identified. In ternary relationship,
where three entities are involved, in this as well another relation is created to establish
relationship among them. Then finally we had studied the super and sub types in
which primary key of super type was used for both identity and link.
Data Manipulation Languages
This is the third component of relational data model. We have studied structure,
which is the relation, integrity constraints both referential and entity integrity
constraint. Data manipulation languages are used to carry out different operations like
insertion, deletion or creation of database. Following are the two types of languages:
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Procedural Languages:
These are those languages in which what to do and how to do on the database is
required. It means whatever operation is to be done on the database that has to be told
that how to perform.
Non -Procedural Languages:
These are those languages in which only what to do is required, rest how to do is done
by the manipulation language itself.
Structured query language (SQL) is the most widely language used for manipulation
of data. But we will first study Relational Algebra and Relational Calculus, which are
procedural and non ­ procedural respectively.
Relational Algebra
Following are few major properties of relational algebra:
o Relational algebra operations work on one or more relations to define
another relation leaving the original intact. It means that the input for
relational algebra can be one or more relations and the output would be
another relation, but the original participating relations will remain
unchanged and intact.Both operands and results are relations, so output from
one operation can become input to another operation. It means that the input
and output both are relations so they can be used iteratively in different
requirements.
o Allows expressions to be nested, just as in arithmetic. This property is called
closure.
o There are five basic operations in relational algebra: Selection, Projection,
Cartesian product, Union, and Set Difference.
o These perform most of the data retrieval operations needed.
o It also has Join, Intersection, and Division operations, which can be expressed
in terms of 5 basic operations.
Exercise:
-
Consider the example given in Ricardo book on page 216 and transform it into
relational data model. Make any necessary assumptions if required.
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Table of Contents:
  1. Introduction to Databases and Traditional File Processing Systems
  2. Advantages, Cost, Importance, Levels, Users of Database Systems
  3. Database Architecture: Level, Schema, Model, Conceptual or Logical View:
  4. Internal or Physical View of Schema, Data Independence, Funct ions of DBMS
  5. Database Development Process, Tools, Data Flow Diagrams, Types of DFD
  6. Data Flow Diagram, Data Dictionary, Database Design, Data Model
  7. Entity-Relationship Data Model, Classification of entity types, Attributes
  8. Attributes, The Keys
  9. Relationships:Types of Relationships in databases
  10. Dependencies, Enhancements in E-R Data Model. Super-type and Subtypes
  11. Inheritance Is, Super types and Subtypes, Constraints, Completeness Constraint, Disjointness Constraint, Subtype Discriminator
  12. Steps in the Study of system
  13. Conceptual, Logical Database Design, Relationships and Cardinalities in between Entities
  14. Relational Data Model, Mathematical Relations, Database Relations
  15. Database and Math Relations, Degree of a Relation
  16. Mapping Relationships, Binary, Unary Relationship, Data Manipulation Languages, Relational Algebra
  17. The Project Operator
  18. Types of Joins: Theta Join, Equi–Join, Natural Join, Outer Join, Semi Join
  19. Functional Dependency, Inference Rules, Normal Forms
  20. Second, Third Normal Form, Boyce - Codd Normal Form, Higher Normal Forms
  21. Normalization Summary, Example, Physical Database Design
  22. Physical Database Design: DESIGNING FIELDS, CODING AND COMPRESSION TECHNIQUES
  23. Physical Record and De-normalization, Partitioning
  24. Vertical Partitioning, Replication, MS SQL Server
  25. Rules of SQL Format, Data Types in SQL Server
  26. Categories of SQL Commands,
  27. Alter Table Statement
  28. Select Statement, Attribute Allias
  29. Data Manipulation Language
  30. ORDER BY Clause, Functions in SQL, GROUP BY Clause, HAVING Clause, Cartesian Product
  31. Inner Join, Outer Join, Semi Join, Self Join, Subquery,
  32. Application Programs, User Interface, Forms, Tips for User Friendly Interface
  33. Designing Input Form, Arranging Form, Adding Command Buttons
  34. Data Storage Concepts, Physical Storage Media, Memory Hierarchy
  35. File Organizations: Hashing Algorithm, Collision Handling
  36. Hashing, Hash Functions, Hashed Access Characteristics, Mapping functions, Open addressing
  37. Index Classification
  38. Ordered, Dense, Sparse, Multi-Level Indices, Clustered, Non-clustered Indexes
  39. Views, Data Independence, Security, Vertical and Horizontal Subset of a Table
  40. Materialized View, Simple Views, Complex View, Dynamic Views
  41. Updating Multiple Tables, Transaction Management
  42. Transactions and Schedules, Concurrent Execution, Serializability, Lock-Based Concurrency Control, Deadlocks
  43. Incremental Log with Deferred, Immediate Updates, Concurrency Control
  44. Serial Execution, Serializability, Locking, Inconsistent Analysis
  45. Locking Idea, DeadLock Handling, Deadlock Resolution, Timestamping rules