2. Definitions
• Data: known facts that can be recorded
• Database: a collection of data
• represents some aspect of the real world
• logically coherent collection (not a random collection)
• designed, built & populated for a specific purpose
• Database Management System: the software
that manages the data
3. DBMSs provide...
• Facilities to:
– Define – specify data types, structures &
constraints for the data to be stored in the
database
– Construct – store the data
– Manipulate – pose queries to retrieve specific
data, update data or generate reports based
on the data
4. Popular Examples
• Company Databases
– employees, departments, projects …
• Airline Reservation Systems
– flights, fares, customers, reservations ..
• Library Databases
– authors, titles, publishers, videos …
• Bank Databases
– accounts, customers ...
5. Schemas & Instances
• Important to distinguish between
– database schema: the description of the
database
– database: the stored data
6. Financial Records
(Company, Type, Name, Date, Amt, NumShares, Broker)
Company Type Name Date Amt NumShares Broker
Trimark Mutual Trimark 01/01/84 49.75 100 C. Harris
Fund Fund
AGF Mutual Foreign 01/01/94 62.25 1000 C. Harris
Fund Equity
7. Database States
• Empty State -- database is empty when we
first define the database schema
• Initial State -- database is first populated or
loaded with data
• Current State -- snapshot in time
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8. DBMS Languages
• DDL: Data Definition Language
– used to define/change the structure of the
database
• DML: Data Manipulation Language
– used to query the database, insert data,
change data or delete data
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9. Classification of DBMSs
• Data Model Classification
– relational, network, hierarchical, object-
oriented …
• Number of users
– single user or multi-user
• Number of Sites
– centralized vs distributed
• Cost of the DBMS
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10. Data Model
• A collection of concepts that can be used
to define the structure (data, data types,
relations and constraints) of a database.
• Examples:
– Entity Relationship model
– Relational Model
– hierarchical & network models
– object-data models
12. Entity-Relationship Model
• Most popular conceptual model for
database design
• Basis for many other models
• Describes the data in a system and how
that data is related
• Describes data as entities, attributes
and relationships
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13. Database requirements
• We must convert the written database
requirements into an E-R diagram
• Need to determine the entities, attributes
and relationships.
– nouns = entities
– adjectives = attributes
– verbs = relationships
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15. Entities
• Entity – basic object of the E-R model
– Represents a “thing” with an independent
existence
– Can exist physically or conceptually
• a professor, a student, a course
• Entity type – used to define a set of
entities with the same properties.
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16. Entity and Entity Types
Name
Number Topic
Entity Type
Course
Number: 3753
Entity
Name: Database Management Systems
Topic: Introduction to DBMSs
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17. Attributes
• Each entity has a set of associated properties
that describes the entity. These properties are
known as attributes.
• Attributes can be:
– Simple or Composite
– Single or Multi-valued
– Stored or Derived
– NULL
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21. Attributes (cont’d)
• NULL attributes have no value
– not 0 (zero)
– not a blank string
• Attributes can be “nullable” where a null
value is allowed, or “not nullable” where
they must have a value.
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22. Primary Keys
Professor Employee ID
• Employee ID is the primary key
• Primary keys must be unique for the
entity in question
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23. Relationships
• defines a set of associations between
various entities
• can have attributes to define them
• are limited by:
– Participation
– Cardinality Ratio
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25. Participation
• Defines if the existence of an entity depends on
it being related to another entity with a
relationship type.
– Partial
– Total
Section part of Course
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26. Cardinality
• The number of relationships that an entity
may participate in.
– 1:1, 1:N, N:M, M:1
N 1
Section part of Course
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27. Weak entity
• Weak entities do not have key attributes of their
own.
• Weak entities cannot exist without another a
relationship to another entity.
• A partial key is the portion of the key that comes
from the weak entity. The rest of the key comes
from the other entity in the relationship.
• Weak entities always have total participation as
they cannot exist without the identifying
relationship.
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28. Weak Entity (cont’d)
Section ID Section
Descriminator
Identifying Relationship part of
Number
Course
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29. Acadia Teaching Database
Design an E-R schema for a database to store info about professors,
courses and course sections indicating the following:
• The name and employee ID number of each professor
• The salary and email address(es) for each professor
• How long each professor has been at the university
• The course sections each professor teaches
• The name, number and topic for each course offered
• The section and room number for each course section
• Each course section must have only one professor
• Each course can have multiple sections
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30. Visual View of the Database
Employee ID Start Date Years Teaching Section ID Room
1 N
Professor teaches Section
Email
N
Salary First
Name Part of
Last
1
Number Course
Topic Name
31. University DB Case Study
• Maintain the following information about
undergraduate students:
– Name, address, student number, date of
birth, year of study, degree program (BA, BSc,
BCS), concentration (Major, Honours, etc) and
department of concentration.
• Note: An address is composed of a street, city,
province and postal code; the student number is
unique for each student
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32. University Case Study (cont’d)
• Maintain information about departments
– Name, code (CS, Phy), office phone, and faculty
members
• Maintain information about courses:
– Course number (3753), title, description,
prerequisites.
• Maintain information about course sections:
– Section (A, B, C), term (X1), slot #, instructor
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33. University Case Study (cont’d)
• Maintain information about faculty:
– Name, rank, employee number, salary, office
number, phone number and email address.
– Note: employee number is unique
• Maintain a program of study for the current
year for each student:
– i.e. courses that each student is enrolled in
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34. Address Street
Number N M
Section Enrolled Student
Number City
Term
Name Province
Slot N
N
1
Teaches DOB Postal Code
Has
Salary Name
1
N
Faculty Number
Number Prereq
Office
1 N
Title Course M
Phone
Start Date Head Member
Description Email
1 1
End Date Code Rank
N
1
Name
Offer Dept
Phone
35. Extended E-R Model
• E-R model is sufficient for traditional
database applications
• Nontraditional applications (CAD,
multimedia) have more complex
requirements
• Can extend traditional E-R diagrams with
semantic data modeling concepts
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36. IS-A Relationship
Name Employee S.S.N.
IS-A
Staff Faculty Teaching Assistant
Position Rank Student #
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37. Specialization & Generalization
• Specialization
– process of taking an entity and creating
several specialized subclasses
• Generalization
– process of taking several related entities
and creating a general superclass
• We will talk mainly of specialization, but
most information will also apply to
generalization
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38. Specialization constraints
• Specializations can be predicate-defined
or attribute-defined or user-defined
• Disjointness constraint – specialization is
disjoint or overlapping
• Completeness constraint – specialization
is total or partial
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39. Predicate-defined subclass
• An attribute value is used to determine the
members of a subclass
• Not all members of every subclass can be
determined by the attribute value
• In the following example, the Pension Plan type
can be used to determine faculty from staff, but
has no effect on students or those who opted out
of the pension plan.
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41. Attribute-defined subclass
• There is one defining attribute for all
subclasses
• Each member of the superclass can be
assigned to the appropriate subclass
based on this one attribute
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43. User-defined subclass
• When there is no condition to automatically
determine membership in a subclass, it must
be done at the discretion of the user.
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44. Disjointness constraint
• Specifies that an entity can be a member
of at most one subclass
• There can be no overlap between the
subclasses
• We use the notation of a d in a circle to
symbolize that the subclasses are disjoint
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45. Disjoint constraint
Name Employee S.S.N.
d
Staff Teaching Assistant
Faculty
Position Rank Student #
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46. Overlap
• Entities are able to belong to more than
one subclass
• Notation is an o inside of a circle
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47. Overlap
Jobtype Employee S.S.N.
A staff member may
o also be a student
Staff Students Faculty
Rank Year Rank
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48. Completeness Constraint
• May be total or partial
• for total, every entity in the superclass
must belong to a subclass
• for partial, entities in the superclass do not
need to be part of any subclass
• notation for total and partial are the same
as in a regular E-R diagram – single and
double lines
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49. Partial
Pension Person S.S.N.
Plan Type
d
Staff Faculty
Rank Rank
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50. Total
Jobtype Employee S.I.N.
o
Staff Students Faculty
Rank Year Rank
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51. Hierarchies and Lattices
• Hierarchies
– a tree-like structure where each subclass
belongs to only one superclass
• Lattices
– a graph-like structure where a subclass can
belong to more than one superclass
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52. Lattice
name Person
o student #
Employee Student
salary
Teaching Assistant course
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54. Union Types and Lattice
• Lattice
– Subset of the Intersection of the superclasses.
– A shared subclass (Teaching Assistant) is the
subclass in two distinct superclass relatioships
• Union Types
– Subset of the unoin of distinct Entity Types
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55. The UNIVERSITY Database
• Consider a UNIVERSITY database that keeps track of students and
their majors, transcripts, and registration as well as of the university’s
course offerings. The database also keeps track of the sponsored
research projects of faculty and graduate students.
• For each person, the database maintains information on the person’s
Name [Name], social security number [Ssn], address [Address], sex
[Sex], and birth date [BDate]. PERSON can be classified as FACULTY
and STUDENT. Specific attributes of FACULTY are rank (assistant,
associate, adjunct, research, visiting, etc.), office [FOffice], office phone
[FPhone], and salary [Salary]. All faculty members are related to the
academic department(s) with which they are affiliated (a faculty member
can be associated with several departments).
• A specific attribute of STUDENT is Class. Each student is also related to
his or her major and minor departments, if known ([MAJOR] and
[MINOR]), to the course sections he or she is currently attending
[REGISTERED], and to the courses completed [TRANSCRIPT]. Each
transcript instance includes the grade the student received in the
course section.
56. • GRAD_STUDENT is a subclass of STUDENT, with the defining predicate
Class = 5. For each graduate student, we keep a list of previous degrees
[Degrees]. We also relate the graduate student to a faculty advisor
[ADVISOR] and to a thesis committee [COMMITTEE], if one exists.
• An academic department has the attributes name [DName], telephone
[DPhone], and office number [Office] and is related to the faculty member
who is its chairperson [CHAIRS] and to the college to which it belongs
[CD]. Each college has attributes college name [CName], office number
[COffice], and the name of its dean [Dean].
• A course has attributes course number [C#], course name [Cname], and
course description [CDesc]. Several sections of each course are offered,
with each section having the attributes section number [Sec#] and the
year and quarter in which the section was offered ([Year] and [Qtr]).
Section numbers uniquely identify each section. The sections being
offered during the current quarter are in a subclass
CURRENT_SECTION of SECTION, with the defining predicate Qtr =
CurrentQtr and Year = CurrentYear. Each section is related to the
instructor who taught or is teaching it ([TEACH]), if that instructor is in the
database.
57. • The category INSTRUCTOR_RESEARCHER is a subset of the
union of FACULTY and GRAD_STUDENT and includes all faculty,
as well as graduate students who are supported by teaching or
research. Finally, the entity type GRANT keeps track of research
grants and contracts awarded to the university. Each grant has
attributes grant title [Title], grant number [No], the awarding agency
[Agency], and the starting date [StDate]. A grant is related to one
principal investigator [PI] and to all researchers it supports
[SUPPORT]. Each instance of support has as attributes the starting
date of support [Start], the ending date of the support (if known)
[End], and the percentage of time being spent on the project [Time]
by the researcher being supported
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59. Private Airport Database
• An EER diagram for a small private airport database that is used to
keep track of airplanes, their owners, airport employees, and pilots.
From the requirements for this database, the following information
was collected: Each AIRPLANE has a registration number [Reg#], is
of a particular plane type [OF_TYPE], and is stored in a particular
hangar [STORED_IN]. Each PLANE_TYPE has a model number
[Model], a capacity [Capacity], and a weight [Weight]. Each
HANGAR has a number [Number], a capacity [Capacity], and a
location [Location].
• The database also keeps track of the OWNERs of each plane
[OWNS] and the EMPLOYEEs who have maintained the plane
[MAINTAIN]. Each relationship instance in OWNS relates an
airplane to an owner and includes the purchase date [Pdate]. Each
relationship instance in MAINTAIN relates an employee to a service
record [SERVICE].
60. • Each plane undergoes service many times; hence, it is related by
[PLANE_SERVICE] to a number of service records. A service record
includes as attributes the date of maintenance [Date], the number of
hours spent on the work [Hours], and the type of work done
[Workcode]. We use an entity type [SERVICE] to represent airplane
service, because the airplane registration number is used to identify
a service record. An owner is either a person or a corporation.
person [PERSON] entity type can be classified as pilots [PILOT]
and employees [EMPLOYEE].
• Each pilot has specific attributes license number [Lic_Num] and
• restrictions [Restr]; each employee has specific attributes salary
[Salary] and shift worked [Shift].
• All PERSON entities in the database have data kept on their social
security number [Ssn], name [Name], address [Address], and
telephone number [Phone].
• For CORPORATION entities, the data kept includes name [Name],
address[Address], and telephone number [Phone].
• The database also keeps track of the types of planes each pilot is
authorized to fly [FLIES] and the types of planes each employee can
do maintenance work on [WORKS_ON].
62. Relationships of Higher Degree
q Relationship types of degree 2 are called binary
q Relationship types of degree 3 are called ternary
and of degree n are called n-ary
q In general, an n-ary relationship is not equivalent to
n binary relationships
Chapter 3-62
65. Problem with constraints on
higher order relationship types
m
n
p
What does it mean to put m:n:p on the three arms of the relationship ?
It is essentially meaningless.
Chapter 3-65
67. The (min,max) notation for
higher order relationship type
constraints
(1,2) (1,3)
(1,5)
A Teacher can offer min 1 and max 2 Offerings
A Course may have 1 to 3 Offerings
A Student may enroll in from 1 to 5 Offerings
Chapter 3-67
