Data: facts concerning people, objects, events or other
entities Structured: numbers, text, dates Unstructured: images,
video, documents Information: data that are processed to be useful;
answers "who", "what", "where", and "when" questions Knowledge: the
appropriate collection of information, such that it's intent is to
be useful; answers "how" questions Understanding: appreciation of
why Wisdom: evaluated understanding Data, Information, Knowledge,
and Wisdom 2
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File Systems Traditionally composed of collection of file
folders kept in file cabinet Organization within folders was based
on datas expected use (ideally logically related) System was
adequate for small amounts of data with few reporting requirements
Finding and using data in growing collections of file folders
became time consuming and cumbersome 3
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File Systems (cont.) 4
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Advantages of File Systems No resource overhead No cost
overhead Speed to access data Disadvantages of File Systems Data
redundancy and inconsistency Difficulty in access data and process
data Lack of standardizations Hard to maintenance and update data
Security problems, etc. 5
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Database System and Database Management System (DBMS) 6
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Database System (cont.) Shared collection of logically related
data (and a description of this data), designed to meet the
information needs of an organization. System catalog (metadata,
data dictionary) provides description of data to enable programdata
independence. Logically related data comprises entities,
attributes, and relationships of an organizations information.
7
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Database Management System (DBMS) A software system that
enables users to define, create, maintain, and control access to
the database. (Database) application program: a computer program
that interacts with database by issuing an appropriate request (SQL
statement) to the DBMS. 8
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Components of DBMS Environment 9
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Advantages of DBMSs Control of data redundancy Data consistency
More information from the same amount of data Sharing of data
Improved data integrity Improved security Enforcement of standards
Economy of scale Multiple applications on 1 set of data 10
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Advantages of DBMSs Balance conflicting requirements DBA makes
decision about the design and operational use of database in order
to achieve the optimal performance Improved data accessibility and
responsiveness Increased productivity Improved maintenance through
data independence Increased concurrency Improved backup and
recovery service 11
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Disadvantages of DBMSs Complexity Size Cost of DBMS Additional
hardware costs Cost of conversion Performance DBMS is written to be
more general (as opposed to being specific to a certain type of
application), so it may not run as fast as the file-based systems.
Higher impact of a failure single point of failure 12
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ANSI-SPARC Three-Level Architecture 13
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Database Architecture (cont.) External Level Users view of the
database. Describes that part of database that is relevant to a
particular user. Conceptual Level Community view of the database.
Describes what data is stored in database and relationships among
the data. Internal Level Physical representation of the database on
the computer. Describes how the data is stored in the database.
14
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Differences between Three Levels of ANSI-SPARC Architecture
15
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Benefit of 3-level Architecture: Data Independence Logical Data
Independence Capacity to change conceptual schema without having to
change external schema or application programs Physical Data
Independence Capacity to change the internal schema without having
to change the conceptual (or external) schemas 16
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Data Independence and the ANSI- SPARC Three-Level Architecture
17
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Data Model Integrated collection of concepts for describing
data, relationships between data, and constraints on the data in an
organization. Data Model comprises: A structural part; which
database can be constructed A manipulative part; types of allowed
operation A set of integrity rules; ensuring accuracy of data
18
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Data Model (Cont.) Purpose To represent data in an
understandable way. Categories of data models include: Physical
Record-based Object-based 19
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Data Model Physical Data Models Record-Based Data Models
Hierarchical Data Model Network Data Model Relational Data Model
Object-Based Data Models Entity-Relationship Data Model
Object-Oriented Data Model etc. 20
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Hierarchical Data Model 21
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Network Data Model 22
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Relational Data Model 23
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Entity Relationship Data Model 24
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Object Oriented Data Model 25
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Evolution of Data Models 26
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ER Diagram of Branch User Views of DreamHome Pearson Education
Limited 1995, 2005 ER Data Modeling 27
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Concepts of the ER Model Entity types Relationship types
Attributes Pearson Education Limited 1995, 2005 28
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Entity Type Entity type Group of objects with same properties,
identified by enterprise as having an independent existence. Entity
occurrence Uniquely identifiable object of an entity type. Pearson
Education Limited 1995, 2005 29
Slide 30
ER diagram of Staff and Branch Entity Types Pearson Education
Limited 1995, 2005 30
Slide 31
Relationship Types Relationship type Set of meaningful
associations among entity types. Relationship occurrence Uniquely
identifiable association, which includes one occurrence from each
participating entity type. Pearson Education Limited 1995, 2005
31
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Semantic net of Has Relationship Type Pearson Education Limited
1995, 2005 32
Slide 33
ER diagram of Branch Has Staff Relationship Pearson Education
Limited 1995, 2005 33
Slide 34
Relationship Types Degree of a Relationship Number of
participating entities in relationship. Relationship of degree: Two
is binary Three is ternary Four is quaternary. Pearson Education
Limited 1995, 2005 34
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Binary Relationship called POwns Pearson Education Limited
1995, 2005 35
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Ternary Relationship called Registers Pearson Education Limited
1995, 2005 36
Slide 37
Quaternary Relationship called Arranges Pearson Education
Limited 1995, 2005 37
Slide 38
Relationship Types Recursive Relationship Relationship type
where same entity type participates more than once in different
roles. Relationships may be given role names to indicate purpose
that each participating entity type plays in a relationship.
Pearson Education Limited 1995, 2005 38
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Recursive Relationship called Supervises with Role Names
Pearson Education Limited 1995, 2005 39
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Entities Associated through Two Distinct Relationships with
Role Names Pearson Education Limited 1995, 2005 40
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Attributes Attribute Property of an entity or a relationship
type. Attribute Domain Set of allowable values for one or more
attributes. Simple Attribute Attribute composed of a single
component with an independent existence. Composite Attribute
Attribute composed of multiple components, each with an independent
existence. Pearson Education Limited 1995, 2005 41
Slide 42
Attributes Single-valued Attribute Attribute that holds a
single value for each occurrence of an entity type. Multi-valued
Attribute Attribute that holds multiple values for each occurrence
of an entity type. Derived Attribute Attribute that represents a
value that is derivable from value of a related attribute, or set
of attributes, not necessarily in the same entity type. Pearson
Education Limited 1995, 2005 42
Slide 43
Keys Candidate Key Minimal set of attributes that uniquely
identifies each occurrence of an entity type. Primary Key Candidate
key selected to uniquely identify each occurrence of an entity
type. Composite Key A candidate key that consists of two or more
attributes. Pearson Education Limited 1995, 2005 43
Slide 44
ER Diagram of Staff and Branch Entities and Their Attributes
Pearson Education Limited 1995, 2005 44
Slide 45
Entity Type Strong Entity Type Entity type that is not
existence-dependent on some other entity type. Weak Entity Type
Entity type that is existence-dependent on some other entity type.
Pearson Education Limited 1995, 2005 45
Slide 46
Relationship called Advertises with Attributes Pearson
Education Limited 1995, 2005 46
Slide 47
Structural Constraints Main type of constraint on relationships
is called multiplicity. Multiplicity - number (or range) of
possible occurrences of an entity type that may relate to a single
occurrence of an associated entity type through a particular
relationship. Represents policies (called business rules)
established by user or company. The most common degree for
relationships is binary. Binary relationships are generally
referred to as being: one-to-one (1:1) one-to-many (1:*)
many-to-many (*:*) Pearson Education Limited 1995, 2005 47
Slide 48
Semantic Net of Staff Manages Branch Relationship Type Pearson
Education Limited 1995, 2005 48
Structural Constraints Multiplicity for Complex Relationships
Number (or range) of possible occurrences of an entity type in an
n-ary relationship when other (n-1) values are fixed. Pearson
Education Limited 1995, 2005 54
Slide 55
Semantic Net of Ternary Registers Relationship with Values for
Staff and Branch Entities Fixed Pearson Education Limited 1995,
2005 55
Summary of Multiplicity Constraints Pearson Education Limited
1995, 2005 57
Slide 58
Structural Constraints Multiplicity is made up of two types of
restrictions on relationships: cardinality and participation.
Cardinality Describes maximum number of possible relationship
occurrences for an entity participating in a given relationship
type. Participation Determines whether all or only some entity
occurrences participate in a relationship. Pearson Education
Limited 1995, 2005 58
Slide 59
Multiplicity as Cardinality and Participation Constraints
Pearson Education Limited 1995, 2005 59
Slide 60
Problems with ER Models Problems may arise when designing a
conceptual data model called connection traps. Often due to a
misinterpretation of the meaning of certain relationships. Two main
types of connection traps are called fan traps and chasm traps. Fan
Trap Where a model represents a relationship between entity types,
but pathway between certain entity occurrences is ambiguous. Chasm
Trap Where a model suggests the existence of a relationship between
entity types, but pathway does not exist between certain entity
occurrences. Pearson Education Limited 1995, 2005 60
Slide 61
An Example of a Fan Trap Pearson Education Limited 1995, 2005
61
Slide 62
Semantic Net of ER Model with Fan Trap At which branch office
does staff number SG37 work? Pearson Education Limited 1995, 2005
62
Slide 63
Restructuring ER Model to Remove Fan Trap Pearson Education
Limited 1995, 2005 63
Slide 64
Semantic Net of Restructured ER Model with Fan Trap Removed
SG37 works at branch B003. Pearson Education Limited 1995, 2005
64
Slide 65
An Example of a Chasm Trap Pearson Education Limited 1995, 2005
65
Slide 66
Semantic Net of ER Model with Chasm Trap At which branch office
is property PA14 available? Pearson Education Limited 1995, 2005
66
Slide 67
ER Model Restructured to Remove Chasm Trap Pearson Education
Limited 1995, 2005 67
Slide 68
Semantic Net of Restructured ER Model with Chasm Trap Removed
Pearson Education Limited 1995, 2005 68
Slide 69
Degrees of Relationship, Alternative Representation Pearson
Education Limited 1995, 2005 69
Slide 70
Object-Oriented Data Modeling What Is Object-Oriented Data
Modeling? Centers around objects and classes Involves inheritance
Encapsulates both data and behavior Benefits of Object-Oriented
Modeling Ability to tackle challenging problems Improved
communication between users, analysts, designers, and programmers
Increased consistency in analysis, design, and programming Explicit
representation of commonality among system components System
robustness Reusability of analysis, design, and programming results
2009 Pearson Education, Inc. Publishing as Prentice Hall70
Slide 71
Classes and Objects Class: An entity that has a well-defined
role in the application domain, as well as state, behavior, and
identity Tangible: person, place or thing Concept or Event:
department, performance, marriage, registration Artifact of the
Design Process: user interface, controller, scheduler Object: a
particular instance of a class Objects exhibit BEHAVIOR as well as
attributes Different from entities 2009 Pearson Education, Inc.
Publishing as Prentice Hall71
Slide 72
State, Behavior, Identity State: attribute types and values
Behavior: how an object acts and reacts Behavior is expressed
through operations that can be performed on it Identity: every
object has a unique identity, even if all of its attribute values
are the same 2009 Pearson Education, Inc. Publishing as Prentice
Hall72
Slide 73
UML Class and Object Diagram Class diagram shows the static
structure of an object-oriented model: object classes, internal
structure, relationships 2009 Pearson Education, Inc. Publishing as
Prentice Hall73
Slide 74
Operation A function or service that is provided by all
instances of a class Types of operations: Constructor: creates a
new instance of a class Query: accesses the state of an object but
does not alter its state Update: alters the state of an object
Scope: operation applying to the class instead of an instance
Operations implement the objects behavior 2009 Pearson Education,
Inc. Publishing as Prentice Hall74
Slide 75
Associations Association: Named relationship among object
classes Association Role: Role of an object in an association The
end of an association where it connects to a class Multiplicity:
How many objects participate in an association. Lower-
boundUpper-bound (cardinality) 2009 Pearson Education, Inc.
Publishing as Prentice Hall75
Slide 76
Examples of Association Relationships of Different Degree
Lower-bound upper-bound Represented as: 0..1, 0..*, 1..1, 1..*
Similar to minimum/maximum cardinality rules in EER Unary Binary
Ternary 2009 Pearson Education, Inc. Publishing as Prentice
Hall76
Slide 77
Association Class An association that has attributes or
operations of its own or that participates in relationships with
other classes Like an associative entity in E-R model 2009 Pearson
Education, Inc. Publishing as Prentice Hall77
Slide 78
Class Diagram Showing Association Classes Registration class
implements a many-to-many association between Student and Course
2009 Pearson Education, Inc. Publishing as Prentice Hall78
Slide 79
Generalization/Specialization Subclass, superclass similar to
subtype/supertype in EER Common attributes, relationships, and
operations Disjoint vs. Overlapping Complete (total specialization)
vs. incomplete (partial specialization) Abstract Class: no direct
instances possible, but subclasses may have direct instances
Concrete Class: direct instances possible 2009 Pearson Education,
Inc. Publishing as Prentice Hall79
Slide 80
Examples of Generalization, Inheritance, and Constraints - a)
Employee Superclass with Three Subclasses Shared attributes and
operations An employee can only be one of these subclasses An
employee may be none of them Specialized attributes and operations
80
Slide 81
Examples of Generalization, Inheritance, and Constraints b)
Abstract Patient Class with Two Concrete Subclasses Abstract
indicated by italics A patient MUST be EXACTLY one of the subtypes
Dynamic means a patient can change from one subclass to another
over time 2009 Pearson Education, Inc. Publishing as Prentice
Hall81
Slide 82
Polymorphism Abstract Operation: Defines the form or protocol
of the operation, but not its implementation Method: The
implementation of an operation Polymorphism: The same operation may
apply to two or more different classes in different ways 2009
Pearson Education, Inc. Publishing as Prentice Hall82
Slide 83
Class-Scope Attribute Specifies a value common to an entire
class, rather than a specific value for an instance. Represented by
underlining = is initial, default value 2009 Pearson Education,
Inc. Publishing as Prentice Hall83
Slide 84
Polymorphism, Abstract Operation, Class-Scope Attribute, and
Ordering Class-scope attributes only one value common to all
instances of these classes (includes default values) This operation
is abstractit has no method at Student level Methods are defined at
subclass level 84
Slide 85
Aggregation Aggregation: A part-of relationship between a
component object and an aggregate object Composition: A stronger
form of aggregation in which a part object belongs to only one
whole object and exists only as part of the whole object Recursive
Aggregation: Composition where component object is an instance of
the same class as the aggregate object 2009 Pearson Education, Inc.
Publishing as Prentice Hall85
Slide 86
Example of Aggregation A Personal Computer includes CPU, Hard
Disk, Monitor, and Keyboard as parts. But, these parts can exist
without being installed into a computer. The open diamond indicates
aggregation, but not composition 2009 Pearson Education, Inc.
Publishing as Prentice Hall86
Slide 87
References Hoffer et. al. Modern Database Management, The Tenth
Edition, Pearson. Education, 2011. Kroenke and Auer. Database
Concepts, 3 rd Edition, Upper Saddle River, N.J.: Pearson Prentice
Hall, 2008. Elmasri and Navathe. Fundamentals of Database Systems,
The Fifth Edition, Pearson. Education, Inc., 2007. 87