Enhanced Entity Relationship model
(EER)
Origin of ERD – Chen 76.
1.
Motivation

Develop a data-model that is close to reality.
 Has a real world ontology (compare with the
relational model).
 Used mainly for database design.
 Recent motivation: data integration.
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2.
Intuitive presentation:
Example – an EERD for a medical clinic.
name
ID
Employee
phone
(1,1)
(0,1)
Department
dep-name
manages
x
start-date
license-no
specialization
(1,1)
works-for
(0,N)
address
ID
name
supervises
(0,1)
degree
supervision
supervised-by
Physician
(0,N)
(1,N)
name
Patient
Nurse
(1,1)
job-description
(1,1)
address
phone
(1,1)
School
city
patient visits
(1,2)
treating
date
study
physician schedules
(0,N)
(2,3)
(1,20)
date
Visit
diagnosis
Profession
code
(0,N)
description
name
Schedule
day
2
(0,N)
from-time
med-code
no-of-days
(0,N)
to-time
Medicine
prescriptions
qty-per-day
Ontology – the world view:
The real world portion captured by the knowledge
model (EER schema in our case).

Named finite sets of entities – the entity types (classes).
An entity type is identified by its name (a constraint).

Named relations on the sets of entities – the relationship
types. Each relation has an arity. Each entity set
participating in a relation has a role name (not always
given). A relationship type is identified by its name (a
constraint).

The entities and the related entities (tuples) might have
named attributes. An attribute assigns values to the
entities/tuples in an entity/relationship set. An attribute of an
entity/relationship type is identified by its name (a
constraint).

Attributes are named functions (mappings) from
entity/relationship types to value domains. The names
are unique for a type.
Attribute classification:
1.
Single / multi valued. The values of a multivalued attribute are sets (the value domain is a set of
sets).
2.
Simple / composite. A composite attribute is
associated with a sequence of attributes. Its values are
tuples of the values of the attributes in the sequence.
Restriction: The association is acyclic (a constraint).
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
An entity type might contain other entity types – the
generalization/sub-typing relations.
Restrictions:
1.
The sub-typing structure is acyclic (a
constraint).
2.
An entity type is directly contained in at
most a single entity type.

Questions that are left open:
1.
Are the entity types that are not related by subtyping (directly or indirectly) disjoint?
2.
Can two named entity types be “a single set of
entities with two names”?
3.
Can two named relationship types be “a single
relation with two names”?
Other questions …?
4.
4

1.
Constraints:
Key constraints:
One.
Most entity types have an attribute (or
attributes) whose values identify the entities. This
attribute(s) is called a key attribute.
 The key is a 1:1 function from the entity set to the
value domain of the key attributes (either the key
attribute is a 1:1 function, or the key attributes
combination forms a 1:1 tuple function).
Such entity types are called strong. Entity types that are
not strong are called weak.
Two.
Weak entity types: The entities in entity types
that are not strong are identified by their relationships
with entities of other entity types. Each weak entity type
E is associated with binary identifying relationship
types, that relate E with its owner entity types. The key
constraint for E is:
1.
Every identifying relationship type
relates an E entity to a single owner entity.
2.
Some attributes of a weak entity type
may be marked as partial key attributes.
3.
Every E entity is uniquely identified by its
owner entities and its partial key values.
 The combination of the owner entity mapping and
the partial key attributes combination forms a 1:1
function.
Questions that are left open – are there restrictions on
ownership structures:
1.
Can a single entity type function as a
multiple owner type?
2.
Are cyclic ownership structures legal?
3.
Are ownership structures necessarily
directed trees?
Three.
Relationship types might also have key
attributes.
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2.
Cardinality constraints – impose number
restrictions on relationship types.
Some versions:
1.
For every physician, the number of
different visits related through treating is limited
between 1 to 20.
Entity look-across cardinality constraint.
2. a. Every profession must be represented
in the clinic.
Two.
Every physician must have
studied some profession in some school.
Entity participation cardinality constraint.
a. Every physician that studied a
profession, must have studied it in at
most 2 schools.
b. Every physician that studied in some
school, must have studied at least 2
professions and no more than 3 professions.
Relationship look-across cardinality constraint.
3.
a. If a school is represented in the clinic,
then it must be related to at least 2
physician-profession entity tuples.
b. If a medicine is prescripted, it must be
prescripted at least 5 times and no more than
10 times.
Relationship participation cardinality constraint.
4.
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Is that enough?
Is an ontological specification of a
data model sufficient?
YES – for human (manual) manipulation:





Conceptual database design.
Manual software specification.
Maintenance aid.
Client-modeler communication.
Requirements specification.
Needed – specification of visual language:
 Labeled rectangles.
 Labeled diamonds –
connected by lines to rectangles.
role name labels.
cardinality labels.
 Labeled circles –
each connected by a line to a rectangle or
to a diamond or to another circle.
 Labeled dashed rectangles -connected by directed lines to dashed
diamonds.
 Lines between rectangles.
+ the constraints.
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For automatic checking of the syntactical correctness
of an EER diagram:
 Formal visual grammar – Spatial Relations
Specification (SRS):
Symbols: rectangles, diamonds, circles, lines,
texts, arrows, dashed rectangles,
dashed diamonds.
Sentences: R-E, E-E, A-A, E-A, CardC, NamedR,
…
Definitions of sentences:
R-E := line & at-least 4 touching
& exactly-one touching CardC
& exactly-one touching Rectangle
& exactly-one touching Diamond
& exactly-one touching NamedR.
 Formal specification of the constraints.
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Is that enough?
Is an ontological specification of a
data model sufficient?
NO – for automatic processing:
 Software synthesis (e.g., Rapsody).
 Automatic conceptual database design.
 Schema
reasoning:
Satisfiability,
correction, optimization, equivalence and
transformations.
 Data integration, reverse engineering.
 Model extensions:
Concrete domains – e.g., Time.
Hierarchy.
 Dynamic enhancements:
Consistency enforcement.
Add active elements like triggers.
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Needed:
 Abstract
(logical) Syntax
Specification (ASS) – specifies
syntactically correct schemas.
 Declarative semantics – the
ontological denotation (e.g., a
database instance) of the
syntactical schema.
 Consistency specification –
constrains the legal denotations
(which database instances are
legal).
A
schema
is
consistent
(satisfiable) if it has a consistent
denotation.
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Knowledge -- Visual Specification
A Diagramatic visual language: Collection of diagrams.
A diagram: Graphical objects with well-defined meaning.
USER: Physical layout of a diagram + meaning of the diagram.
Implementation: Needs connection between the 2 representations:
Spatial relations specification (SRS) – describes the diagram.
Abstract syntax specification (ASS).
PRODUCTION APPLICATIONS
create
create
represents
Spatial Relations
Specification
Abstract Syntax
Specification
Represented by
Graphical
scanning
Constraint
solving
interpretation
Physical layout

Physical layout: lines, circles, colors, exact positions, line kinds,
text fragments, …

Spatial relations specification (SRS): replace individual properties
of objects by spatial relations between objects, like: touches, contains,
labels, left-of.
Defines the pictorial structure of the diagram.
Forms a constraint system that specifies the correctness of the physical
layout.

Abstract syntax specification (ASS): Diagram content is
abstracted from the visual representation (like the EER specification).
Specifies logical constraints.
Each construct in the SRS represents a construct(s) in the ASS, and each
construct in the ASS is represented by a construct(s) in the SRS.

Meaning: The ontology described by the diagram.
Reference:
K. Marriott and B. Meyer: Visual Language Theory, Springer-Verlag, 1998.
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Ontology