Introduction to ER Diagrams

Database Design using E-R Diagram
Adri Jovin J J, M.Tech., Ph.D.
UITG004- INTRODUCTION TO DATABASE MANAGEMENT SYSTEMS

Design Phases
 Initial phase -- characterize fully the data needs of the prospective database users.
 Second phase -- choosing a data model
• Applying the concepts of the chosen data model
• Translating these requirements into a conceptual schema of the database.
• A fully developed conceptual schema indicates the functional requirements of the enterprise.
 Describe the kinds of operations (or transactions) that will be performed on the data.
October 8, 2021 UITG004 - Introduction to Database Management Systems 4

Design Phases (Cont.)
 Final Phase -- Moving from an abstract data model to the implementation of the database
• Logical Design – Deciding on the database schema.
• Database design requires that we find a “good” collection of relation schemas.
 Business decision – What attributes should we record in the database?
 Computer Science decision – What relation schemas should we have and how should
the attributes be distributed among the various relation schemas?
• Physical Design – Deciding on the physical layout of the database
5

Design Alternatives
In designing a database schema, we must ensure that we avoid two major pitfalls:
• Redundancy: a bad design may result in repeat information.
 Redundant representation of information may lead to data inconsistency among the various copies of
information
• Incompleteness: a bad design may make certain aspects of the enterprise difficult or impossible to model.
Avoiding bad designs is not enough. There may be a large number of good designs from which we must
choose.
6

Design Approaches
Entity Relationship Model
• Models an enterprise as a collection of entities and relationships
 Entity: a “thing” or “object” in the enterprise that is distinguishable from other objects
• Described by a set of attributes
 Relationship: an association among several entities
• Represented diagrammatically by an entity-relationship diagram:
Normalization Theory
• Formalize what designs are bad, and test for them
7

Entity Sets
 An entity is an object that exists and is distinguishable from other objects.
• Example: specific person, company, event, plant
 An entity set is a set of entities of the same type that share the same properties.
• Example: set of all persons, companies, trees, holidays
 An entity is represented by a set of attributes; i.e., descriptive properties possessed by all members of an
entity set.
• Example:
instructor = (ID, name, salary )
course= (course_id, title, credits)
 A subset of the attributes form a primary key of the entity set; i.e., uniquely identifying each member of the
set.

Representing Entity sets in ER Diagram
 Entity sets can be represented graphically as follows:
• Rectangles represent entity sets.
• Attributes listed inside entity rectangle
• Underline indicates primary key attributes
12

Relationship Sets
 A relationship is an association among several entities
Example:
44553 (Peltier) advisor 22222 (Einstein)
student entity relationship set instructor entity
 A relationship set is a mathematical relation among n  2 entities, each taken from entity sets
{(e1, e2, … en) | e1  E1, e2  E2, …, en  En}
where (e1, e2, …, en) is a relationship
• Example:
(44553,22222)  advisor

Relationship Sets (Cont.)
 Example: we define the relationship set advisor to denote the associations between students and the
instructors who act as their advisors.
 Pictorially, we draw a line between related entities.
14

Representing Relationship Sets via ER Diagrams
 Diamonds represent relationship sets.
15

Relationship Sets (Cont.)
 An attribute can also be associated with a relationship set.
 For instance, the advisor relationship set between entity sets instructor and student may have the attribute
date which tracks when the student started being associated with the advisor
instructor
student
76766 Crick
Katz
Srinivasan
Kim
Singh
Einstein
45565
10101
98345
76543
22222
98988
12345
00128
76543
44553
Tanaka
Shankar
Zhang
Brown
Aoi
Chavez
Peltier
3 May 2008
10 June 2007
12 June 2006
6 June 2009
30 June 2007
31 May 2007
4 May 2006
76653
23121

Relationship Sets with Attributes

Roles
 Entity sets of a relationship need not be distinct
• Each occurrence of an entity set plays a “role” in the relationship
 The labels “course_id” and “prereq_id” are called roles.

Degree of a Relationship Set
 Binary relationship
• involve two entity sets (or degree two).
• most relationship sets in a database system are binary.
 Relationships between more than two entity sets are rare. Most relationships are binary. (More on this later.)
• Example: students work on research projects under the guidance of an instructor.
• relationship proj_guide is a ternary relationship between instructor, student, and project

Non-binary Relationship Sets
 Most relationship sets are binary
 There are occasions when it is more convenient to represent relationships as non-binary.
 E-R Diagram with a Ternary Relationship

Complex Attributes
 Attribute types:
• Simple and composite attributes.
• Single-valued and multivalued attributes
 Example: multivalued attribute: phone_numbers
• Derived attributes
 Can be computed from other attributes
 Example: age, given date_of_birth
 Domain – the set of permitted values for each attribute

Composite Attributes
 Composite attributes allow us to divided attributes into subparts (other attributes).
name address
first_name middle_initial last_name street city state postal_code
street_number street_name apartment_number
composite
attributes
component
attributes

Representing Complex Attributes in ER Diagram
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Mapping Cardinality Constraints
 Express the number of entities to which another entity can be associated via a relationship set.
 Most useful in describing binary relationship sets.
 For a binary relationship set the mapping cardinality must be one of the following types:
• One to one
• One to many
• Many to one
• Many to many

Mapping Cardinalities
One to one One to many
Note: Some elements in A and B may not be mapped to any
elements in the other set
25

Mapping Cardinalities
Many to one Many to many
Note: Some elements in A and B may not be mapped to any
elements in the other set
26

Representing Cardinality Constraints in ER Diagram
 We express cardinality constraints by drawing either a directed line (), signifying “one,” or an undirected
line (—), signifying “many,” between the relationship set and the entity set.
 One-to-one relationship between an instructor and a student :
• A student is associated with at most one instructor via the relationship advisor
• A student is associated with at most one department via stud_dept

One-to-Many Relationship
 one-to-many relationship between an instructor and a student
• an instructor is associated with several (including 0) students via advisor
• a student is associated with at most one instructor via advisor,

Many-to-One Relationships
 In a many-to-one relationship between an instructor and a student,
• an instructor is associated with at most one student via advisor,
• and a student is associated with several (including 0) instructors via advisor

Many-to-Many Relationship
 An instructor is associated with several (possibly 0) students via advisor
 A student is associated with several (possibly 0) instructors via advisor

Total and Partial Participation
 Total participation (indicated by double line): every entity in the entity set participates in at least one
relationship in the relationship set
participation of student in advisor relation is total
 every student must have an associated instructor
 Partial participation: some entities may not participate in any relationship in the relationship set
• Example: participation of instructor in advisor is partial
31

Notation for Expressing More Complex Constraints
 A line may have an associated minimum and maximum cardinality, shown in the form l..h, where l is the
minimum and h the maximum cardinality
• A minimum value of 1 indicates total participation.
• A maximum value of 1 indicates that the entity participates in at most one relationship
• A maximum value of * indicates no limit.
 Example
• Instructor can advise 0 or more students. A student must have 1 advisor; cannot have multiple advisors
32

Cardinality Constraints on Ternary Relationship
 We allow at most one arrow out of a ternary (or greater degree) relationship to indicate a cardinality
constraint
 For example, an arrow from proj_guide to instructor indicates each student has at most one guide for a
project
 If there is more than one arrow, there are two ways of defining the meaning.
• For example, a ternary relationship R between A, B and C with arrows to B and C could mean
1. Each A entity is associated with a unique entity from B
and C or
2. Each pair of entities from (A, B) is associated with a unique C entity, and each pair (A, C) is
associated with a unique B
• Each alternative has been used in different formalisms
• To avoid confusion we outlaw more than one arrow

Primary Key
 Primary keys provide a way to specify how entities and relations are distinguished. We will consider:
• Entity sets
• Relationship sets.
• Weak entity sets

Primary key for Entity Sets
 By definition, individual entities are distinct.
 From database perspective, the differences among them must be expressed in terms of their attributes.
 The values of the attribute values of an entity must be such that they can uniquely identify the entity.
• No two entities in an entity set are allowed to have exactly the same value for all attributes.
 A key for an entity is a set of attributes that suffice to distinguish entities from each other

Primary Key for Relationship Sets
 To distinguish among the various relationships of a relationship set we use the individual primary keys of the
entities in the relationship set.
• Let R be a relationship set involving entity sets E1, E2, .. En
• The primary key for R is consists of the union of the primary keys of entity sets E1, E2, ..En
• If the relationship set R has attributes a1, a2, .., am associated with it, then the primary key of R also
includes the attributes a1, a2, .., am
 Example: relationship set “advisor”.
• The primary key consists of instructor.ID and student.ID
 The choice of the primary key for a relationship set depends on the mapping cardinality of the relationship
set.

Choice of Primary key for Binary Relationship
 Many-to-Many relationships. The preceding union of the primary keys is a minimal superkey and is chosen as
the primary key.
 One-to-Many relationships . The primary key of the “Many” side is a minimal superkey and is used as the
primary key.
 Many-to-one relationships. The primary key of the “Many” side is a minimal superkey and is used as the primary
key.
 One-to-one relationships. The primary key of either one of the participating entity sets forms a minimal
superkey, and either one can be chosen as the primary key.

Weak Entity Sets
 Consider a section entity, which is uniquely identified by a course_id, semester, year, and sec_id.
 Clearly, section entities are related to course entities. Suppose we create a relationship set sec_course
between entity sets section and course.
 Note that the information in sec_course is redundant, since section already has an attribute course_id, which
identifies the course with which the section is related.
 One option to deal with this redundancy is to get rid of the relationship sec_course; however, by doing so the
relationship between section and course becomes implicit in an attribute, which is not desirable.

Weak Entity Sets (Cont.)
 An alternative way to deal with this redundancy is to not store the attribute course_id in the section entity
and to only store the remaining attributes section_id, year, and semester.
• However, the entity set section then does not have enough attributes to identify a particular section
entity uniquely
 To deal with this problem, we treat the relationship sec_course as a special relationship that provides extra
information, in this case, the course_id, required to identify section entities uniquely.
 A weak entity set is one whose existence is dependent on another entity, called its identifying entity
 Instead of associating a primary key with a weak entity, we use the identifying entity, along with extra
attributes called discriminator to uniquely identify a weak entity.

Weak Entity Sets (Cont.)
 An entity set that is not a weak entity set is termed a strong entity set.
 Every weak entity must be associated with an identifying entity; that is, the weak entity set is said to be existence
dependent on the identifying entity set.
 The identifying entity set is said to own the weak entity set that it identifies.
 The relationship associating the weak entity set with the identifying entity set is called the identifying relationship.
 Note that the relational schema we eventually create from the entity set section does have the attribute course_id,
for reasons that will become clear later, even though we have dropped the attribute course_id from the entity set
section.

Expressing Weak Entity Sets
 In E-R diagrams, a weak entity set is depicted via a double rectangle.
 We underline the discriminator of a weak entity set with a dashed line.
 The relationship set connecting the weak entity set to the identifying strong entity set is depicted by a double
diamond.
 Primary key for section – (course_id, sec_id, semester, year)

Redundant Attributes
 Suppose we have entity sets:
• student, with attributes: ID, name, tot_cred, dept_name
• department, with attributes: dept_name, building, budget
 We model the fact that each student has an associated department using a relationship set stud_dept
 The attribute dept_name in student below replicates information present in the relationship and is therefore
redundant
• and needs to be removed.
 BUT: when converting back to tables, in some cases the attribute gets reintroduced, as we will see later.

E-R Diagram for a University Enterprise
43

Reduction to Relation Schemas

Reduction to Relation Schemas
 Entity sets and relationship sets can be expressed uniformly as relation schemas that represent the contents
of the database.
 A database which conforms to an E-R diagram can be represented by a collection of schemas.
 For each entity set and relationship set there is a unique schema that is assigned the name of the
corresponding entity set or relationship set.
 Each schema has a number of columns (generally corresponding to attributes), which have unique names.

Redundancy of Schemas
 Many-to-one and one-to-many relationship sets that are total on the many-side can be represented by adding
an extra attribute to the “many” side, containing the primary key of the “one” side
 Example: Instead of creating a schema for relationship set inst_dept, add an attribute dept_name to the
schema arising from entity set instructor
 Example
46

Redundancy of Schemas (Cont.)
For one-to-one relationship sets, either side can be chosen to act as the “many” side
• That is, an extra attribute can be added to either of the tables corresponding to the two entity sets
If participation is partial on the “many” side, replacing a schema by an extra attribute in the schema corresponding
to the “many” side could result in null values
47

Specialization
 Top-down design process; we designate sub-groupings within an entity set that are distinctive from other
entities in the set.
 These sub-groupings become lower-level entity sets that have attributes or participate in relationships that do
not apply to the higher-level entity set.
 Depicted by a triangle component labeled ISA (e.g., instructor “is a” person).
 Attribute inheritance – a lower-level entity set inherits all the attributes and relationship participation of the
higher-level entity set to which it is linked.

Specialization Example
 Overlapping – employee and student
 Disjoint – instructor and secretary
 Total and partial

Representing Specialization via Schemas
Method 1:
• Form a schema for the higher-level entity
• Form a schema for each lower-level entity set, include primary key of higher-level entity set and local attributes
• Drawback: getting information about, an employee requires accessing two relations, the one corresponding to the low-
level schema and the one corresponding to the high-level schema
51

Representing Specialization as Schemas (Cont.)
Method 2:
• Form a schema for each entity set with all local and inherited attributes
• Drawback: name, street and city may be stored redundantly for people who are both students and employees
52

Generalization
 A bottom-up design process – combine a number of entity sets that share the same features into a higher-
level entity set.
 Specialization and generalization are simple inversions of each other; they are represented in an E-R
diagram in the same way.
 The terms specialization and generalization are used interchangeably.

Completeness constraint
 Completeness constraint -- specifies whether or not an entity in the higher-level entity set must belong to at
least one of the lower-level entity sets within a generalization.
• total: an entity must belong to one of the lower-level entity sets
• partial: an entity need not belong to one of the lower-level entity sets

Completeness constraint (Cont.)
 Partial generalization is the default.
 We can specify total generalization in an ER diagram by adding the keyword total in the diagram and drawing a
dashed line from the keyword to the corresponding hollow arrow-head to which it applies (for a total
generalization), or to the set of hollow arrow-heads to which it applies (for an overlapping generalization).
 The student generalization is total: All student entities must be either graduate or undergraduate. Because the
higher-level entity set arrived at through generalization is generally composed of only those entities in the lower-
level entity sets, the completeness constraint for a generalized higher-level entity set is usually total

Aggregation
 Consider the ternary relationship proj_guide, which we saw earlier
 Suppose we want to record evaluations of a student by a guide on a project
56

Aggregation (Cont.)
 Relationship sets eval_for and proj_guide represent overlapping information
• Every eval_for relationship corresponds to a proj_guide relationship
• However, some proj_guide relationships may not correspond to any eval_for relationships
 So we can’t discard the proj_guide relationship
 Eliminate this redundancy via aggregation
• Treat relationship as an abstract entity
• Allows relationships between relationships
• Abstraction of relationship into new entity

Aggregation (Cont.)
Eliminate this redundancy via aggregation without introducing redundancy, the following
diagram represents:
• A student is guided by a particular instructor on a particular project
• A student, instructor, project combination may have an associated evaluation
58

Reduction to Relational Schemas
 To represent aggregation, create a schema containing
• Primary key of the aggregated relationship,
• The primary key of the associated entity set
• Any descriptive attributes
 In our example:
• The schema eval_for is:
eval_for (s_ID, project_id, i_ID, evaluation_id)
• The schema proj_guide is redundant.
59

Common Mistakes in E-R Diagrams
Example of erroneous E-R diagrams
61

Common Mistakes in E-R Diagrams (Cont.)
62

Entities vs. Attributes
Use of entity sets vs. attributes
Use of phone as an entity allows extra information about phone numbers (plus
multiple phone numbers)
63

Entities vs. Relationship sets
Use of entity sets vs. relationship sets
Possible guideline is to designate a relationship set to describe
an action that occurs between entities
Placement of relationship attributes
For example, attribute date as attribute of advisor or as attribute
of student
64

Binary Vs. Non-Binary Relationships
Although it is possible to replace any non-binary (n-ary, for n > 2) relationship set by a number
of distinct binary relationship sets, a n-ary relationship set shows more clearly that several
entities participate in a single relationship.
Some relationships that appear to be non-binary may be better represented using binary
relationships
• For example, a ternary relationship parents, relating a child to his/her father and mother, is
best replaced by two binary relationships, father and mother
• Using two binary relationships allows partial information (e.g., only mother being
known)
• But there are some relationships that are naturally non-binary
• Example: proj_guide
65

Converting Non-Binary Relationships to Binary Form
In general, any non-binary relationship can be represented using binary relationships by creating an
artificial entity set.
• Replace R between entity sets A, B and C by an entity set E, and three relationship sets:
1. RA, relating E and A 2. RB, relating E and B
3. RC, relating E and C
• Create an identifying attribute for E and add any attributes of R to E
• For each relationship (ai , bi , ci) in R, create
1. a new entity ei in the entity set E 2. add (ei , ai ) to RA
3. add (ei , bi ) to RB 4. add (ei , ci ) to RC
66

Converting Non-Binary Relationships (Cont.)
Also need to translate constraints
• Translating all constraints may not be possible
• There may be instances in the translated schema that
cannot correspond to any instance of R
• Exercise: add constraints to the relationships RA, RB and RC to ensure that a newly created entity
corresponds to exactly one entity in each of entity sets A, B and C
• We can avoid creating an identifying attribute by making E a weak entity set (described shortly) identified
by the three relationship sets
67

E-R Design Decisions
 The use of an attribute or entity set to represent an object.
 Whether a real-world concept is best expressed by an entity set or a relationship set.
 The use of a ternary relationship versus a pair of binary relationships.
 The use of a strong or weak entity set.
 The use of specialization/generalization – contributes to modularity in the design.
 The use of aggregation – can treat the aggregate entity set as a single unit without
concern for the details of its internal structure.

Summary of Symbols Used in E-R Notation
69

Symbols Used in E-R Notation (Cont.)

Alternative ER Notations
 Chen, IDE1FX, …

Alternative ER Notations
Chen IDE1FX (Crows feet notation)

UML
 UML: Unified Modeling Language
 UML has many components to graphically model different aspects of an
entire software system
 UML Class Diagrams correspond to E-R Diagram, but several
differences.

ER vs. UML Class Diagrams
* Note reversal of position in cardinality constraint depiction

ER Diagram Notation Equivalent in UML
* Generalization can use merged or separate arrows independent
of disjoint/overlapping

UML Class Diagrams (Cont.)
 Binary relationship sets are represented in UML by just drawing a line
connecting the entity sets. The relationship set name is written adjacent to the
line.
 The role played by an entity set in a relationship set may also be specified by
writing the role name on the line, adjacent to the entity set.
 The relationship set name may alternatively be written in a box, along with
attributes of the relationship set, and the box is connected, using a dotted line,
to the line depicting the relationship set.

Other Aspects of Database Design
 Functional Requirements
 Data Flow, Workflow
 Schema Evolution

References
Abraham Silberschatz, Henry F. Korth & Sudharshan S (2010). Database System Concepts. 6th Edition, Tata McGraw
Hill.
Acknowledgement:
 The content in this presentation is adopted from the website https://www.db-book.com/. This presentation is used for instructional purpose and not for any
purpose involving monetary profits.
 This presentation adheres to CC BY-SA 4.0, provided any user deriving the contents acknowledge the original authors Abraham Silberschatz, Henry F. Korth &
Sudharshan S
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October 8, 2021 UITG004 - Introduction to Database Management Systems

Introduction to ER Diagrams

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