1. Database Systems Background Review (2) Dr. Muhammad Shafique
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Database Systems Background Review

2. Database Systems Background Review
Outline
Database Development
Data Modeling
ER Model
EER Model
Relational Data Model
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Database Systems Background Review

3. Overview of Database Design Process
Two main activities:
Database design
Applications design
To design the conceptual schema for a database application
To design the programs and interfaces that interact with the database
Generally considered part of software engineering
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Database Systems Background Review

4. Database Systems Background Review
Database Development
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Database Systems Background Review

5. Database Systems Background Review
Data Modeling
Categories of data models
Conceptual (high-level, semantic) data models
Provide concepts that are close to the way many users perceive data.
Also called entity-based or object-based data models
Implementation (representational) data models
Provide concepts that fall between the above two, used by many commercial DBMS implementations
Relational data model
Physical (low-level, internal) data models
Provide concepts that describe details of how data is stored in the computer.
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Database Systems Background Review

6. Data Modeling Using ER Model (1)
ER Model Concepts
Entity
Entity Types, Value Sets, and Key Attributes
Weak Entity Types
Attribute
Categories of Attributes
Simple
Composite
Multi-valued
Relationship
Relationship Types
Roles and Attributes in Relationship Types
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Database Systems Background Review

7. Data Modeling Using ER Model (2)
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8. Relationships of Higher Degree
Relationship types of degree 2 are called binary
Relationship types of degree 3 are called ternary and of degree n are called n-ary
In general, an n-ary relationship is not equivalent to n binary relationships
Constraints are harder to specify for higher-degree relationships (n > 2) than for binary relationships
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Database Systems Background Review

9. Ternary Relationships
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10. Another example of a ternary relationship
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Database Systems Background Review

11. Some of the Currently Available Automated Database Design Tools
COMPANY
TOOL
FUNCTIONALITY
Embarcadero Technologies
ER Studio
Database Modeling in ER and IDEF1X
DB Artisan
Database administration, space and security management
Oracle
Developer 2000/Designer 2000
Database modeling, application development
Popkin Software
System Architect 2001
Data modeling, object modeling, process modeling, structured analysis/design
Platinum (Computer Associates)
Enterprise Modeling Suite: Erwin, BPWin, Paradigm Plus
Data, process, and business component modeling
Persistence Inc.
Pwertier
Mapping from O-O to relational model
Rational (IBM)
Rational Rose
UML Modeling & application generation in C++/JAVA
Resolution Ltd.
Xcase
Conceptual modeling up to code maintenance
Sybase
Enterprise Application Suite
Data modeling, business logic modeling
Visio
Visio Enterprise
Data modeling, design/reengineering Visual Basic/C++
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Database Systems Background Review

12. Enhanced-ER (EER) Model (1)
EER stands for Enhanced ER or Extended ER
EER Model Concepts
Includes all modeling concepts of basic ER
Additional concepts:
Subclasses/superclasses
Specialization/generalization
Categories (UNION types)
Attribute and relationship inheritance
The additional EER concepts are used to model applications more completely and more accurately
EER includes some object-oriented concepts, such as inheritance
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Database Systems Background Review

13. Database Systems Background Review
Subclasses and Superclasses
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Database Systems Background Review

14. Database Systems Background Review
Specialization
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Database Systems Background Review

15. Database Systems Background Review
Generalization
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Database Systems Background Review

16. Specialization/Generalization
Constraints
Two basic constraints on specialization/generalization
Disjointness Constraint:
Completeness Constraint
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17. Example of disjoint partial Specialization
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18. Database Systems Background Review
Example of overlapping total Specialization
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Database Systems Background Review

19. Specialization / Generalization Lattice Example (UNIVERSITY)
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20. Example of Categorization (Union Type)
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21. Formal Definitions of EER Model (1)
Class C:
A type of entity with a corresponding set of entities:
The definition of relationship type in ER/EER should have 'entity type' replaced with 'class‘ to allow relationships among classes in general
Subclass S is a class whose:
Type inherits all the attributes and relationship of a class C
Set of entities must always be a subset of the set of entities of the other class C
S ⊆ C
C is called the superclass of S
A superclass/subclass relationship exists between S and C
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Database Systems Background Review

22. Formal Definitions of EER Model (2)
Specialization Z: Z = {S1, S2,…, Sn} is a set of subclasses with same superclass G
Hence, G/Si is a superclass relationship for i = 1, …., n.
G is called a generalization of the subclasses {S1, S2,…, Sn}
Z is total if we always have:
S1 ∪ S2 ∪ … ∪ Sn = G;
Otherwise, Z is partial.
Z is disjoint if we always have:
Si ∩ S2 empty-set for i ≠ j;
Otherwise, Z is overlapping.
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23. Formal Definitions of EER Model (3)
Subclass S of C is predicate defined if predicate (condition) p on attributes of C is used to specify membership in S;
that is, S = C[p], where C[p] is the set of entities in C that satisfy condition p
A subclass not defined by a predicate is called user-defined
Attribute-defined specialization: if a predicate A = ci (where A is an attribute of G and ci is a constant value from the domain of A) is used to specify membership in each subclass Si in Z
Note: If ci ≠ cj for i ≠ j, and A is single-valued, then the attribute-defined specialization will be disjoint.
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24. Formal Definitions of EER Model (4)
Category or UNION type T
A class that is a subset of the union of n defining superclasses D1, D2,…Dn, n > 1:
T ⊆ (D1 ∪ D2 ∪ … ∪ Dn)
Can have a predicate pi on the attributes of Di to specify entities of Di that are members of T.
If a predicate is specified on every Di
T = (D1[p1] ∪ D2[p2] ∪…∪ Dn[pn])
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Database Systems Background Review

25. Conceptual Modeling Concepts
Semantic data models and Knowledge Representation (KR)
Similarities
Both use an abstraction process
Both provide concepts, constraints. Operations, and languages for defining data and representing knowledge
Differences
KR is broader in scope than semantic data models
KR schemes include reasoning mechanisms
KR schemes mix up schemas with instances whereas semantic data models focus on the representation of database schemas
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26. Conceptual Modeling Concepts
Abstraction concepts
Classification and Instantiation
Is-an-instance-of or is-a-member-of
Exceptions
Class properties
Meta-class concept
Identification
Two levels of identification
To distinguish among database objects and classes
To identify database objects and to relate them to their real-world counterparts ( same person in different roles)
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Database Systems Background Review

27. Conceptual Modeling Concepts
Abstraction concepts
Specialization and generalization
Is-a or Is-a-subclass-of
CONSTRAINTS
CARDINALITY (Min and Max)
COVERAGE (Total vs. Partial, and Exclusive (disjoint) vs. Overlapping)
Aggregation and association
To build composite objects
Three scenarios
Entity as an aggregation of attributes
Association relationship to relate objects from several independent classes (explained with example in the following slides)
Combining related objects to define higher level objects
IS-A-PART-OF or IS-A-COMPONENT-OF
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Database Systems Background Review

28. Modeling Aggregation (1)
Example: Company, Job_applicant, and Job_Offer
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29. Modeling Aggregation (2)
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30. Modeling Aggregation (3)
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Database Systems Background Review

31. Modeling Aggregation (4)
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32. Modeling Aggregation (5)
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33. Database Systems Background Review
Ontologies
Ontology is “a specification of a conceptualization”
Specification refers to the language and vocabulary terms (data model concepts) used to specify conceptualization
Conceptualization refers to the set of concepts that are used represent the reality or knowledge that is of interest to a community of users
Some possible descriptions of ontology
Dictionary --- describes relationships between words
Database schems
Many medical, scientific, and engineering ontologies are being developed as a means of standardizing concepts and terminology
Semantic Web --- for meaningful information exchange
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Database Systems Background Review

34. Database Systems Background Review
References
Textbook Chapter 3
Textbook Chapter 4
Semantic Database Modeling: Survey, Applications, and Research Issues Richard Hull and Roger King ACM Computing Surveys, Vol. 19, No. 3, September 1987
Semantic Data Models Joan Peckham and Fred Maryanski ACM Computing Surveys, Vol. 20, No. 3, September 1988, pp 153 – 189
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Database Systems Background Review

35. Database Systems Background Review
Summary
Data Modeling
ER Model
EER Model
Other data models (reading material)
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Database Systems Background Review