Enhanced EntityRelationship Modeling

1
Chapter 12

• Enhanced Entity-Relationship Modeling

2
Chapter 12 - Objectives

• Most useful additional data modeling concepts of
  Enhanced ER (EER) model called
• specialization/generalization
• aggregation
• composition.
• A diagrammatic technique for displaying
  specialization/generalization, aggregation, and
  composition in an EER diagram using UML.

3
Why An Enhanced Entity-Relationship Model?

• More demanding requirements
• CAD
• GIS
• OIS
• More complex applications.
• Need for additional semantic modeling concepts.

4
The Enhanced Entity-Relationship (ERR) Model

• Examples of additional concepts of EER model are
• specialization / generalization
• aggregation
• composition.

5
Specialization / Generalization

• Superclass
• An entity type that includes one or more distinct
  subgroupings of its occurrences.
• Subclass
• A distinct subgrouping of occurrences of an
  entity type.

6
Specialization / Generalization

• Superclass/subclass relationship is one-to-one
  (11).
• Superclass may contain overlapping or distinct
  subclasses.
• Not all members of a superclass need be a member
  of a subclass.

7
Specialization / Generalization

• Subclass Attribute Inheritance
• An entity inherits all the attributes of its
  superclass
• An entity also has subclass specific attributes

8
Specialization / Generalization

• Specialization
• Top-Down approach
• Begin with a Superclass
• Identify differences between members of an entity
  by examining their distinguishing attributes
• Group distinguishing attributes in subclasses
• Generalization
• Bottom-Up Approach
• Begin with entities (subclass candidates)
• Identify attributes that are common between
  subclass
• Group like attributes in a superclass

9
AllStaff Relation Holding Details of all Staff
Specialization
10
Specialization/Generalization of Staff Entity
into Subclasses Representing Job Roles
11
Specialization/Generalization of Staff Entity
into Job Roles and Contracts of Employment
12
EER Diagram with Shared Subclass and Subclass
with its own Subclass
13
Constraints on Specialization / Generalization

• Participation constraint
• Determines whether every member in superclass
  must participate as a member of a subclass.
• May be mandatory or optional.
• Disjoint constraint
• Describes relationship between members of the
  subclasses
• indicates whether member of a superclass can be a
  member of one, or more than one, subclass.
• May be disjoint (or) or nondisjoint (and)

14
DreamHome Worked Example - Staff Superclass with
Supervisor and Manager Subclasses
15
DreamHome Worked Example - Owner Superclass with
PrivateOwner and BusinessOwner Subclasses
16
DreamHome Worked Example - Person Superclass with
Staff, PrivateOwner, and Client Subclasses
17
EER Diagram of Branch View of DreamHome with
Specialization/Generalization
18
Aggregation / Composition

• Semantic notation only
• Aggregation
• Represents a has-a or is-part-of relationship
  between entity types, where one represents the
  whole and the other the part.
• Composition
• Specific form of aggregation that represents an
  association between entities, where there is a
  strong ownership and coincidental lifetime
  between the whole and the part

19
Examples of Aggregation/Composition
Aggregation
Composition
20
Chapter 13

• Normalization

21
Normalization

• Determine good design of database model
• Accurate representation of data, its
  relationships, and constraints.
• To achieve this objective, must identify a
  suitable set of relations.

22
Normalization

• Four most commonly used normal forms
• first (1NF)
• second (2NF)
• third (3NF) normal forms
• BoyceCodd normal form (BCNF).
• Based on functional dependencies among the
  attributes of a relation.
• A relation can be normalized to a specific form
  to prevent possible occurrence of update
  anomalies.

23
Data Redundancy

• Major aim of relational database design
• minimize data redundancy
• reduce file storage space
• Problems associated with data redundancy are
• Update anomalies
• Insert anomalies
• Deletion anomalies

24
Data Redundancy
Redundant Data
25
Lossless-join and Dependency Preservation
Properties

• Decomposing original relation into multiple
  relations creates a model that avoids update
  anomalies
• Two important properties of decomposition
• - Lossless-join property enables us to
• Recreate the original relation joining instances
  of the smaller relations.
• - Dependency preservation property enables us to
• enforcing a constraint on original relation by
  enforcing some constraint on each of the smaller
  relations.

26
Functional Dependency

• Main concept associated with normalization.
• Functional Dependency
• Describes relationship between attributes in a
  relation
• If A and B are attributes of relation R, B is
  functionally dependent on A (denoted A ? B)
• if each value of A in R is associated with
  exactly one value of B in R.

27
Functional Dependency

• Property of the meaning (or semantics) of the
  attributes in a relation.
• Diagrammatic representation

• Determinant of a functional dependency refers to
  attribute or group of attributes on left-hand
  side of the arrow.

28
Example - Functional Dependency
29
Functional Dependency

• A property of a relational schema (intension)
  not
• A property of a particular instance of the schema
  (extension)

30
Functional Dependency

• Main characteristics of functional dependencies
  used in normalization
• have a 11 relationship between attribute(s) on
  left and right-hand side of a dependency
• hold for all time
• are nontrivial.

31
Functional Dependency

• Complete set of functional dependencies for a
  given relation can be very large.
• Important to find an approach that can reduce set
  to a manageable size.
• Need to identify a set of functional dependencies
  on (X) that is
• smaller than complete set of functional
  dependencies (Y)
• has property that every functional dependency in
  Y is implied by functional dependencies in X.

32
Functional Dependency

• Set of all functional dependencies implied by a
  given set of functional dependencies X called
  closure of X (written X).
• Set of inference rules, called Armstrongs
  axioms, specifies how new functional dependencies
  can be inferred from given ones.

33
Functional Dependency

• Let A, B, and C be subsets of the attributes of
  relation R. Armstrongs axioms are as
  follows 1. Reflexivity
• If B is a subset of A, then A B
• 2. Augmentation
• If A B, then A,C B,C
• 3. Transitivity
• If A B and B C, then A C
• Complete - through application of axioms on X all
  functional dependencies of Y can be derived
• Sound no additional dependencies can be derived
  that are not implied by X

34
Normalization

• Formal technique for analyzing a relation
• Based on primary key and functional dependencies
• Series of Steps
• With each successive step the relation gets more
  restricted

35
First Normal Form (1NF)

• A relation in which intersection of each row and
  column contains one and only one value.
• 1NF a relation that contains no repeating groups

36
UNF to 1NF

• Nominate an attribute or group of attributes to
  act as the key for the unnormalized table.
• Identify repeating group(s) in unnormalized table
  which repeats for the key attribute(s).
• Remove repeating group by
• entering appropriate data into the empty columns
  of rows containing repeating data (flattening
  the table).
• Or by
• placing repeating data along with copy of the
  original key attribute(s) into a separate
  relation.

37
Second Normal Form (2NF)

• Based on concept of full functional dependency
• A and B are attributes of a relation,
• B is fully dependent on A if
• B is functionally dependent on A but not on any
  proper subset of A.
• 2NF - A relation that is in 1NF and every
  non-primary-key attribute is fully functionally
  dependent on the primary key.
• Relations with a simple key are automatically in
  2NF
• Need to concentrate on relations with composite
  keys

38
1NF to 2NF

• Identify primary key for the 1NF relation.
• Identify functional dependencies in the relation.
• If partial dependencies exist on the primary key
  remove them by placing them in a new relation
  along with copy of their determinant.

39
Third Normal Form (3NF)

• Based on concept of transitive dependency
• A, B and C are attributes of a relation such that
  if A ? B and B ? C,
• then C is transitively dependent on A through B.
  (Provided that A is not functionally dependent on
  B or C).
• 3NF - A relation that is in 1NF and 2NF and in
  which no non-primary-key attribute is
  transitively dependent on the primary key.
• No non-key attribute is a determinant for for
  another non-key attribute

40
2NF to 3NF

• Identify the primary key in the 2NF relation.
• Identify functional dependencies in the relation.
• If transitive dependencies exist on the primary
  key remove them by placing them in a new relation
  along with copy of their determinant.

41
General Definitions of 2NF and 3NF

• Second normal form (2NF)
• A relation that is in 1NF and every
  non-primary-key attribute is fully functionally
  dependent on any candidate key.
• Third normal form (3NF)
• A relation that is in 1NF and 2NF and in which no
  non-primary-key attribute is transitively
  dependent on any candidate key.

42
BoyceCodd Normal Form (BCNF)

• Based on functional dependencies that take into
  account all candidate keys in a relation, however
  BCNF also has additional constraints compared
  with general definition of 3NF.
• BCNF - A relation is in BCNF if and only if every
  determinant is a candidate key.

43
BoyceCodd normal form (BCNF)

• Difference between 3NF and BCNF is that for a
  functional dependency A ? B, 3NF allows this
  dependency in a relation if B is a primary-key
  attribute and A is not a candidate key.
• Whereas, BCNF insists that for this dependency to
  remain in a relation, A must be a candidate key.
• Every relation in BCNF is also in 3NF. However,
  relation in 3NF may not be in BCNF.

44
BoyceCodd normal form (BCNF)

• Violation of BCNF is quite rare.
• Potential to violate BCNF may occur in a relation
  that
• contains two (or more) composite candidate keys
• the candidate keys overlap (i.e. have at least
  one attribute in common).

45
Review of Normalization (UNF to BCNF)
46
Review of Normalization (UNF to BCNF)
47
Review of Normalization (UNF to BCNF)
48
Review of Normalization (UNF to BCNF)
49
Fourth Normal Form (4NF)

• Although BCNF removes anomalies due to functional
  dependencies, another type of dependency called a
  multi-valued dependency (MVD) can also cause data
  redundancy.
• Possible existence of MVDs in a relation is due
  to 1NF and can result in data redundancy.

50
Fourth Normal Form (4NF) - MVD

• Dependency between attributes (for example, A, B,
  and C) in a relation, such that for each value of
  A there is a set of values for B and a set of
  values for C. However, set of values for B and C
  are independent of each other.
• MVD between attributes A, B, and C in a relation
  using the following notation
• A ¾¾ØØ B
• A ¾¾ØØ C

51
Fourth Normal Form (4NF)

• MVD can be further defined as being trivial or
  nontrivial.
• MVD A ¾¾ØØ B in relation R is defined as
  being trivial if
• (a) B is a subset of A or
• (b) A ? B R.
• MVD is defined as being nontrivial if neither
  (a) nor (b) are satisfied.
• Trivial MVD does not specify a constraint on a
  relation, while a nontrivial MVD does specify a
  constraint.

52
Fourth Normal Form (4NF)

• Defined as a relation that is in BCNF and
  contains no nontrivial MVDs.

53
4NF - Example
54
Fifth Normal Form (5NF)

• A relation decomposed into two relations must
  have lossless-join property, which ensures that
  no spurious tuples are generated when relations
  are reunited through a natural join.
• However, there are requirements to decompose a
  relation into more than two relations.
• Although rare, these cases are managed by join
  dependency and fifth normal form (5NF).

55
Fifth Normal Form (5NF)

• A relation that has no join dependency.

56
5NF - Example
57
Chapter 14

• Methodology -
• Conceptual Database Design

58
Design Methodology

• Structured approach that uses procedures,
  techniques, tools, and documentation aids to
  support and facilitate the process of design.
• Database design methodology has 3 main phases
• Conceptual database design
• Logical database design
• Physical database design.

59
Database Design

• Conceptual database design
• Process of constructing a model of information
  used in an enterprise, independent of all
  physical considerations.
• Logical database design
• Process of constructing a model of information
  used in an enterprise based on a specific data
  model (e.g. relational), but independent of a
  particular DBMS and other physical
  considerations.

• Physical database design
• Process of producing a description of the
  implementation of the database on secondary
  storage it describes the base relations, file
  organizations, and indexes design used to achieve
  efficient access to the data, and any associated
  integrity constraints and security measures.

60
Critical Success Factors in Database Design

• Work interactively with users as much as
  possible.
• Follow a structured methodology throughout the
  data modeling process.
• Employ a data-driven approach.
• Incorporate structural and integrity
  considerations into the data models.
• Combine conceptualization, normalization, and
  transaction validation techniques into the data
  modeling methodology.

61
Critical Success Factors in Database Design

• Use diagrams to represent as much of the data
  models as possible.
• Use a Database Design Language (DBDL) to
  represent additional data semantics.
• Build a data dictionary to supplement the data
  model diagrams.
• Be willing to repeat steps.

62
Methodology Overview - Conceptual Database Design

• Step 1 Build local conceptual data model for
  each user view
• Step 1.1 Identify entity types
• To identify the main entity types that are
  required by the view
• Step 1.2 Identify relationship types
• To identify the important relationships that
  exist between the entity types that have been
  identified
• Step 1.3 Identify and associate attributes with
  entity or relationship types
• To identify and associate attributes with the
  appropriate entity or relationship types and
  document the details of each attribute.

63
Methodology Overview - Conceptual Database Design
(cont)

• Step 1 Build local conceptual data model for
  each user view
• Step 1.4 Determine attribute domains
• To determine domains for the attributes in the
  local conceptual model and document the details
  of each domain
• Step 1.5 Determine candidate and primary key
  attributes
• To identify the candidate key(s) for each entity
  and if there is more than one candidate key, to
  choose one to be the primary key
• Step 1.6 Consider use of enhanced modeling
  concepts (optional step)
• To consider the use of enhanced modeling
  concepts, such as specialization /
  generalization, aggregation, and composition

64
Methodology Overview - Conceptual Database Design
(cont)

• Step 1 Build local conceptual data model for
  each user view
• Step 1.7 Check model for redundancy
• To check for the presence of any redundancy in
  the model.
• Step 1.8 Validate local conceptual model
  against user transactions
• To ensure that the local conceptual model
  supports the transactions required by the view
• Step 1.9 Review local conceptual data model
  with user
• To review the local conceptual data model with
  the user to ensure that the model is a true
  representation of the users view of the
  enterprise

65
Extract from Data Dictionary for Staff View of
DreamHome Showing Description of Entities
66
First-cut ER diagram for Staff View of DreamHome
67
Extract from Data Dictionary for Staff View of
DreamHome Showing Description of Relationships
68
Extract from Data Dictionary for Staff View of
DreamHome Showing Description of Attributes
69
ER Diagram for Staff View of DreamHome with
Primary Keys Added
70
Revised ER Diagram for Staff View of DreamHome
with Specialization / Generalization
71
Example of a Non-Redundant Relationship FatherOf
72
Using Pathways to Check that the Conceptual Model
Supports the User Transactions
73
(No Transcript)