1 CSE202 Database Management SystemsLecture #4 Prepared & Presented by Asst. Prof. Dr. Samsun M. BAŞARICI

2 Learning Objectives Apply high-level conceptual data modelingCreating a sample database Understand and apply DB terms Differentiate between ER and UML notations

3 Outline Using high-level conceptual data models for database designA sample database application Entity types, entity sets, attributes, and keys Relationship types, relationship sets, roles, and structural constraints Weak entity types Refining the ER design for the COMPANY database ER diagrams, naming conventions, and design issues Example of other Notation: UML class diagrams Relationship types of degree higher than two

4 Data Modeling Using the Entity-Relationship (ER) ModelPopular high-level conceptual data model ER diagrams Diagrammatic notation associated with the ER model Unified Modeling Language (UML)

5 Using High-Level Conceptual Data Models for Database DesignRequirements collection and analysis Database designers interview prospective database users to understand and document data requirements Result: data requirements Functional requirements of the application

6 Using High-Level Conceptual Data Models (cont’d.)Conceptual schema Conceptual design Description of data requirements Includes detailed descriptions of the entity types, relationships, and constraints Transformed from high-level data model into implementation data model

7 Using High-Level Conceptual Data Models (cont.)Logical design or data model mapping Result is a database schema in implementation data model of DBMS Physical design phase Internal storage structures, file organizations, indexes, access paths, and physical design parameters for the database files specified

8 A Sample Database ApplicationCOMPANY Employees, departments, and projects Company is organized into departments Department controls a number of projects Employee: store each employee’s name, Social Security number, address, salary, sex (gender), and birth date Keep track of the dependents of each employee

9

10 Entity Types, Entity Sets, Attributes, and KeysER model describes data as: Entities Relationships Attributes

11 Entities and AttributesEntity Thing in real world with independent existence Attributes Particular properties that describe entity Types of attributes: Composite versus simple (atomic) attributes Single-valued versus multivalued attributes Stored versus derived attributes NULL values Complex attributes

12 Entities and Attributes (cont.)

13 Entity Types, Entity Sets, Keys, and Value SetsCollection (or set) of entities that have the same attributes

14 Entity Types, Entity Sets, Keys, and Value Sets (cont.)Key or uniqueness constraint Attributes whose values are distinct for each individual entity in entity set Key attribute Uniqueness property must hold for every entity set of the entity type Value sets (or domain of values) Specifies set of values that may be assigned to that attribute for each individual entity

15 Initial Conceptual Design of the COMPANY Database

16 Relationship Types, Relationship Sets, Roles, and Structural ConstraintsWhen an attribute of one entity type refers to another entity type Represent references as relationships not attributes

17 Relationship Types, Sets, and InstancesRelationship type R among n entity types E1, E2, ..., En Defines a set of associations among entities from these entity types Relationship instances ri Each ri associates n individual entities (e1, e2, ..., en) Each entity ej in ri is a member of entity set Ej

18 Relationship Degree Degree of a relationship typeNumber of participating entity types Binary, ternary Relationships as attributes Think of a binary relationship type in terms of attributes

19

20 Role Names and Recursive RelationshipsRole name signifies role that a participating entity plays in each relationship instance Recursive relationships Same entity type participates more than once in a relationship type in different roles Must specify role name

21

22 Constraints on Binary Relationship TypesCardinality ratio for a binary relationship Specifies maximum number of relationship instances that entity can participate in Participation constraint Specifies whether existence of entity depends on its being related to another entity Types: total and partial

23 Attributes of Relationship TypesAttributes of 1:1 or 1:N relationship types can be migrated to one entity type For a 1:N relationship type Relationship attribute can be migrated only to entity type on N-side of relationship For M:N relationship types Some attributes may be determined by combination of participating entities Must be specified as relationship attributes

24 Weak Entity Types Do not have key attributes of their ownIdentified by being related to specific entities from another entity type Identifying relationship Relates a weak entity type to its owner Always has a total participation constraint

25 Refining the ER Design for the COMPANY DatabaseChange attributes that represent relationships into relationship types Determine cardinality ratio and participation constraint of each relationship type

26 ER Diagrams, Naming Conventions, and Design Issues

27 Proper Naming of Schema ConstructsChoose names that convey meanings attached to different constructs in schema Nouns give rise to entity type names Verbs indicate names of relationship types Choose binary relationship names to make ER diagram readable from left to right and from top to bottom

28 Design Choices for ER Conceptual DesignModel concept first as an attribute Refined into a relationship if attribute is a reference to another entity type Attribute that exists in several entity types may be elevated to an independent entity type Can also be applied in the inverse

29 Alternative Notations for ER DiagramsSpecify structural constraints on relationships Replaces cardinality ratio (1:1, 1:N, M:N) and single/double line notation for participation constraints Associate a pair of integer numbers (min, max) with each participation of an entity type E in a relationship type R, where 0 ≤ min ≤ max and max ≥ 1

30

31 Example of Other Notation: UML Class DiagramsUML methodology Used extensively in software design Many types of diagrams for various software design purposes UML class diagrams Entity in ER corresponds to an object in UML

32

33 Example of Other Notation: UML Class Diagrams (cont.)Class includes three sections: Top section gives the class name Middle section includes the attributes; Last section includes operations that can be applied to individual objects

34 Example of Other Notation: UML Class Diagrams (cont.)Associations: relationship types Relationship instances: links Binary association Represented as a line connecting participating classes May optionally have a name Link attribute Placed in a box connected to the association’s line by a dashed line

35 Example of Other Notation: UML Class Diagrams (cont.)Multiplicities: min..max, asterisk (*) indicates no maximum limit on participation Types of relationships: association and aggregation Distinguish between unidirectional and bidirectional associations Model weak entities using qualified association

36 Relationship Types of Degree Higher than TwoDegree of a relationship type Number of participating entity types Binary Relationship type of degree two Ternary Relationship type of degree three

37 Choosing between Binary and Ternary (or Higher-Degree) RelationshipsSome database design tools permit only binary relationships Ternary relationship must be represented as a weak entity type No partial key and three identifying relationships Represent ternary relationship as a regular entity type By introducing an artificial or surrogate key

38

39 Constraints on Ternary (or Higher-Degree) RelationshipsNotations for specifying structural constraints on n-ary relationships Should both be used if it is important to fully specify structural constraints

40 Additional Material Enhanced Entity-Relationship (EER) Model

41 Chapter 8 (Elmasri) OutlineSubclasses, Superclasses, and Inheritance Specialization and Generalization Constraints and Characteristics of Specialization and Generalization Hierarchies Modeling of UNION Types Using Categories A Sample UNIVERSITY EER Schema, Design Choices, and Formal Definitions Example of Other Notation: Representing Specialization and Generalization in UML Class Diagrams Data Abstraction, Knowledge Representation, and Ontology Concepts

42 The Enhanced Entity-Relationship (EER) ModelEnhanced ER (EER) model Created to design more accurate database schemas Reflect the data properties and constraints more precisely More complex requirements than traditional applications

43 Subclasses, Superclasses, and InheritanceEER model includes all modeling concepts of the ER model In addition, EER includes: Subclasses and superclasses Specialization and generalization Category or union type Attribute and relationship inheritance

44 Subclasses, Superclasses, and Inheritance (cont.)Enhanced ER or EER diagrams Diagrammatic technique for displaying these concepts in an EER schema Subtype or subclass of an entity type Subgroupings of entities that are meaningful Represented explicitly because of their significance to the database application

45 Subclasses, Superclasses, and Inheritance (cont.)Terms for relationship between a superclass and any one of its subclasses Superclass/subclass Supertype/subtype Class/subclass relationship Type inheritance Subclass entity inherits all attributes and relationships of superclass

46

47 Specialization and GeneralizationProcess of defining a set of subclasses of an entity type Defined on the basis of some distinguishing characteristic of the entities in the superclass Subclass can define: Specific attributes Specific relationship types

48

49 Specialization and Generalization (cont.)Certain attributes may apply to some but not all entities of the superclass Some relationship types may be participated in only by members of the subclass

50 Generalization Reverse process of abstractionGeneralize into a single superclass Original entity types are special subclasses Generalization Process of defining a generalized entity type from the given entity types

51 Constraints and Characteristics of Specialization and Generalization HierarchiesConstraints that apply to a single specialization or a single generalization Differences between specialization/ generalization lattices and hierarchies

52 Constraints on Specialization and GeneralizationMay be several or one subclass Determine entity subtype: Predicate-defined (or condition-defined) subclasses Attribute-defined specialization User-defined

53 Constraints on Specialization and Generalization (cont.)Disjointness constraint Specifies that the subclasses of the specialization must be disjoint Completeness (or totalness) constraint May be total or partial Disjointness and completeness constraints are independent

54 Specialization and Generalization Hierarchies and LatticesSpecialization hierarchy Every subclass participates as a subclass in only one class/subclass relationship Results in a tree structure or strict hierarchy Specialization lattice Subclass can be a subclass in more than one class/subclass relationship

55

56 Specialization and Generalization Hierarchies and Lattices (cont.)Multiple inheritance Subclass with more than one superclass If attribute (or relationship) originating in the same superclass inherited more than once via different paths in lattice Included only once in shared subclass Single inheritance Some models and languages limited to single inheritance

57 Utilizing Specialization and Generalization in Refining Conceptual SchemasSpecialization process Start with entity type then define subclasses by successive specialization Top-down conceptual refinement process Bottom-up conceptual synthesis Involves generalization rather than specialization

58 Modeling of UNION Types Using CategoriesUnion type or a category Represents a single superclass/subclass relationship with more than one superclass Subclass represents a collection of objects that is a subset of the UNION of distinct entity types Attribute inheritance works more selectively Category can be total or partial Some modeling methodologies do not have union types

59 A Sample UNIVERSITY EER Schema, Design Choices, and Formal DefinitionsThe UNIVERSITY Database Example UNIVERSITY database Students and their majors Transcripts, and registration University’s course offerings

60

61 Design Choices for Specialization/GeneralizationMany specializations and subclasses can be defined to make the conceptual model accurate If subclass has few specific attributes and no specific relationships Can be merged into the superclass

62 Design Choices for Specialization/Generalization (cont.)If all the subclasses of a specialization/generalization have few specific attributes and no specific relationships Can be merged into the superclass Replace with one or more type attributes that specify the subclass or subclasses that each entity belongs to

63 Design Choices for Specialization/Generalization (cont.)Union types and categories should generally be avoided Choice of disjoint/overlapping and total/partial constraints on specialization/generalization Driven by rules in miniworld being modeled

64 Formal Definitions for the EER Model ConceptsClass Set or collection of entities Includes any of the EER schema constructs of group entities Subclass Class whose entities must always be a subset of the entities in another class Specialization Set of subclasses that have same superclass

65 Formal Definitions for the EER Model Concepts (cont.)Generalization Generalized entity type or superclass Predicate-defined Predicate on the attributes of is used to specify which entities in C are members of S User-defined Subclass that is not defined by a predicate

66 Formal Definitions for the EER Model Concepts (cont.)Category Class that is a subset of the union of n defining superclasses Relationship type Any class can participate in a relationship

67 Example of Other NotationRepresenting specialization and generalization in UML class diagrams Basic notation See Figure 8.10 Base class Root superclass Leaf classes Subclasses (leaf nodes)

68

69 Data Abstraction, Knowledge Representation, and Ontology ConceptsGoal of knowledge representation (KR) techniques Accurately model some domain of knowledge Create an ontology that describes the concepts of the domain and how these concepts are interrelated Goals of KR are similar to those of semantic data models Important similarities and differences

70 Classification and InstantiationSystematically assigning similar objects/entities to object classes/entity types Instantiation Inverse of classification Generation and specific examination of distinct objects of a class

71 Classification and Instantiation (cont.)Exception objects Differ in some respects from other objects of class KR schemes allow such class properties One class can be an instance of another class (called a meta-class) Cannot be represented directly in EER model

72 Identification Abstraction processClasses and objects are made uniquely identifiable by means of some identifier Needed at two levels To distinguish among database objects and classes To identify database objects and to relate them to their real-world counterparts

73 Specialization and GeneralizationClassify a class of objects into more specialized subclasses Generalization Generalize several classes into a higher-level abstract class Includes the objects in all these classes

74 Aggregation and AssociationAbstraction concept for building composite objects from their component objects Association Associate objects from several independent classes Main structural distinction When an association instance is deleted Participating objects may continue to exist

75

76

77 Ontologies and the Semantic WebDocuments contain less structure than database information does Semantic Web Allow meaningful information exchange and search among machines Ontology Specification of a conceptualization Specification Language and vocabulary terms used to specify conceptualization

78 Database design methodology & UMLNext Lecture Database design methodology & UML

79 References Ramez Elmasri, Shamkant Navathe; “Fundamentals of Database Systems”, 6th Ed., Pearson, 2014.