Kroenke-Auer-DBP-e14-PPT-Chapter -05

Kroenke-Auer-DBP-e14-PPT-Chapter -05

David M. Kroenke and David J. Auer Database Processing: Fundamentals, Design, and Implementation Chapter Five: Data Modeling with the Entity-Relationship Model Chapter Objectives To understand the two-phase data modeling/database design process To understand the purpose of the data modeling process

To understand entity-relationship (E-R) diagrams To be able to determine entities, attributes, and relationships To be able to create entity identifiers To be able to determine minimum and maximum cardinalities KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-2 Chapter Objectives

To understand variations of the E-R model To understand and be able to use ID-dependent and other weak entities To understand and be able to use supertype/subtype entities To understand and be able to use strong entity patterns To understand and be able to use the ID-dependent association pattern To understand and be able to use the ID-dependent multivalued attribute pattern KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc.

5-3 Chapter Objectives To understand and be able to use the ID-dependent archetype/instance pattern To understand and be able to use the line-item pattern To understand and be able to use the for-use-by pattern To understand and be able to use recursive patterns To understand the iterative nature of the data modeling process To be able to use the data modeling process KROENKE AND AUER - DATABASE PROCESSING, 14th Edition

2016 Pearson Education, Inc. 5-4 Data Modeling Process of creating a abstract representation of the structure of the database The most important task in database development KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc.

The Data Model A data model is a plan or blueprint for a database design. A data model is more generalized and abstract than a database design. It is easier to change a data model then it is to change a database design, so it is the appropriate place to work through conceptual database problems. KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-6

Database Design Main Purpose of Data Modeling Main purposes of data modeling include: to assist in understanding the meaning (semantics) of the data; to facilitate communication about the information requirements. Building data model requires answering questions about entities, relationships,

and attributes. KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-7 Database Design Main Purpose of Data Modeling A data model ensures we understand: - each users perspective of the data; - nature of the data itself, independent of its

physical representations; - use of data across user views. KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-8 Data Modeling in the SDLC The Systems development life cycle (SDLC) as discussed in Appendix B Data modeling occurs in the

Requirements Analysis step of the SDLC The final data model is part of the approved user requirements KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-9 Requirements Analysis Step The requirements analysis step is a process that

starts with the project plan as its input, and produces a set of approved user requirements as its output. Conduct user interviews Evaluate existing systems Determine needed new forms/reports/queries

Identify needed new application features and functions Consider security Create the data model Consider the five components of an information system: hardware, software, data, procedures and people KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. B-10 Requirements Analysis Step Deliverables The requirements analysis deliverables may include:

Data model User requirements document (URD) Statement of Work (SOW) KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. B-11 Data Model = Conceptual Design Books on systems analysis and design often identify three design stages: Conceptual design (conceptual schema)

Logical design (logical schema) Physical design (physical schema) The data model we are discussing is equivalent to the conceptual design as defined in these books. KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-12 Entity-Relationship (E-R) Model The Entity-Relationship (E-R) model is a set of

concepts and graphical symbols that can be used to create conceptual schemas. Versions: Original E-R modelby Peter Chen (1976) Extended E-R modelextensions to the Chen model Information Engineering (IE)by James Martin (1990); uses crows foot notation, is easier to understand, and we will use it IDEF1Xa national standard developed by the National Institute of Standards and Technology [see Appendix C] Unified Modeling Language (UML)by the Object Management Group; it supports object-oriented methodology [see Appendix D] KROENKE AND AUER - DATABASE PROCESSING, 14th Edition

2016 Pearson Education, Inc. 5-13 Entity-Relationship (ER) Modeling ER Modeling is a top-down approach to database design. Entity Relationship (ER) Diagram A detailed, logical representation of the entities, associations and data elements for an organization or business Notation uses three main constructs

Data entities Relationships Attributes KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. Chen Model & Crows Foot Model 5-14 An Example of an Original

E-R Diagram The notation will be explained KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-15 The Geometric Symbols in Original E-R Models

Entities are represented by rectangles. Attributes are represented by ovals, that are connected to the entity by a straight line. Relationships are represented by diamond shaped symbols. The name of the entity (class) or attribute or relationship is usually placed inside the symbol used for that object. (Sometimes, with relationships,

the name is placed adjacent.) The Four Major Components of the Entity Relationship (E-R) Model Entities (really entity sets) Attributes Relationships Identifiers Entities Something that can be identified and the users want to track: Entity classa collection of entities of a

given type Entity instancethe occurrence of a particular entity There are usually many instances of an entity in an entity class. KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-18 CUSTOMER:

The Entity Class and Two Entity Instances KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-19 Attributes Attributes describe an entitys characteristics. All entity instances of a given entity class have the same attributes, but vary in the values of those attributes.

Originally shown in data models as ellipses. Data modeling products today commonly show attributes in rectangular form. KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-20 EMPLOYEE: Attributes in Ellipses KROENKE AND AUER - DATABASE PROCESSING, 14th Edition

2016 Pearson Education, Inc. 5-21 EMPLOYEE: Attributes in Entity Rectangle KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-22 Attributes

Example of entity types and associated attributes: STUDENT: Student_ID, Student_Name, Home_Address, Phone_Number, Major Guidelines for naming attributes: An attribute name is a noun. An attribute name should be unique To make an attribute name unique and clear, each attribute name should follow a standard format Similar attributes of different entity types should use similar but distinguishing names. KROENKE AND AUER - DATABASE PROCESSING, 14th Edition

2016 Pearson Education, Inc. 5-23 Referential Attributes Make Reference to another instance in another table Referential attribute: Ties the lecturer entity to another entity that is department. Name Name

Instance of Lecturer. IdNum IdNum DeptID DeptID Email Email Ali

Ali 105 105 LG LG [email protected] [email protected] Mary Mary

106 106 IT IT [email protected] [email protected] John John

107 107 ENG ENG [email protected] [email protected] Lim Lim 108

108 IT IT [email protected] [email protected] KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-24

Identifiers Identifiers are attributes that name, or identify, entity instances. The identifier of an entity instance consists of one or more of the entitys attributes. Composite identifiers are identifiers that consist of two or more attributes. Identifiers in data models become keys in database designs. Entities have identifiers. Tables (or relations) have keys. KROENKE AND AUER - DATABASE PROCESSING, 14th Edition

2016 Pearson Education, Inc. 5-25 Entity Attribute Display in Data Models KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-26 Relationships

Entities can be associated with one another in relationships: Relationship classes: associations among entity classes Relationship instances: associations among entity instances In the original E-R model, relationships could have attributes, but today this is no longer done. A relationship class can involve two or more entity classes. KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc.

5-27 Relationships Associations Associations between between instances instances of of one one or or more more entity entity types

types that that is is of of interest interest Given Given aa name name that that describes describes its its function. function.

relationship relationship name name is is an an active active or or aa passive passive verb. verb. Relationship name: writes Author

Book An author writes one or more books A book can be written by one or more authors. KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-28 Degree of the Relationship The degree of the relationship is the number of

entity classes in the relationship: One entity has a unary relationship of degree one. Two entities have a binary relationship of degree two. Three entities have a ternary relationship of degree three. KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-29 Unary Relationship

Employee Supervises KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-30 Binary Relationship KROENKE AND AUER - DATABASE PROCESSING, 14th Edition

2016 Pearson Education, Inc. 5-31 Ternary Relationship KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-32 Entities and Tables The principle difference between an entity

and a table (relation) is that you can express a relationship between entities without using foreign keys. This makes it easier to work with entities in the early design process where the very existence of entities and the relationships between them is uncertain. KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-33 Cardinality

Cardinality means count, and is expressed as a number. Maximum cardinality is the maximum number of entity instances that can participate in a relationship. Minimum cardinality is the minimum number of entity instances that must participate in a relationship. KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-34

Maximum Cardinality Maximum cardinality is the maximum number of entity instances that can participate in a relationship. There are three types of maximum cardinality: One-to-One [1:1] One-to-Many [1:N] Many-to-Many [N:M] KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-35

The Three Types of Maximum Cardinality KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-36 Parent and Child Entities In a one-to-many relationship: The entity on the one side of the relationship is called the parent entity or just the parent.

The entity on the many side of the relationship is called the child entity or just the child. In the figure below, EMPLOYEE is the parent and COMPUTER is the child: KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-37 HAS-A Relationships The relationships we have been

discussing are known as HAS-A relationships: Each entity instance has a relationship with another entity instance. An EMPLOYEE has one or more COMPUTERs. A COMPUTER has one assigned EMPLOYEE. KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-38 Minimum Cardinality

Minimum cardinality is the minimum number of entity instances that must participate in a relationship. Minimums are generally stated as either zero or one: IF zero [0] THEN participation in the relationship by the entity is optional, and no entity instance must participate in the relationship. IF one [1] THEN participation in the relationship by the entity is mandatory, and at least one entity instance must participate in the relationship. KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc.

5-39 Indicating Minimum Cardinality As shown in the examples in a following slide: Minimum cardinality of zero [0] indicating optional participation is indicated by placing an oval next to the optional entity. Minimum cardinality of one [1] indicating mandatory (required) participation is indicated by placing a vertical hash mark next to the required entity.

KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-40 Reading Minimum Cardinality Look toward the entity in question: IF you see an oval THEN that entity is optional (minimum cardinality of zero [0]). IF you see a vertical hash mark THEN that entity is mandatory (required) (minimum cardinality of one [ 1]).

KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-41 The Three Types of Minimum Cardinality KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-42

Data Modeling Notation: IE Crows Foot KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-43 Data Modeling Notation: IE Crows Foot 1:N KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc.

5-44 Data Modeling Notation: IE Crows Foot N:M KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-45 ID-Dependent Entities An ID-dependent entity is an entity (child)

whose identifier includes the identifier of another entity (parent). The ID-dependent entity is a logical extension or subunit of the parent: BUILDING : APARTMENT PAINTING : PRINT The minimum cardinality from the ID-dependent entity to the parent is always one. KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-46

ID-Dependent Entities KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-47 Weak Entities A weak entity is an entity whose existence depends upon another entity. All ID-Dependent entities are considered weak.

There are also non-ID-dependent weak entities. The identifier of the parent does not appear in the identifier of the weak child entity. KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-48 Weak Entities KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc.

5-49 Identifying Relationships The solid line connecting an IDdependent entity and its parent is called an identifying relationship. The dashed line connecting strong entities or a non-ID-dependent weak entity to its parent is called a nonidentifying relationship. KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc.

5-50 ID-Dependent and Weak Entities KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-51 Subtype Entities A subtype entity is a special case of a supertype entity:

STUDENT : UNDERGRADATE or GRADUATE The supertype contains all common attributes, while the subtypes contain specific attributes. The supertype may have a discriminator attribute which indicates the subtype. KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-52

Subtypes with Discriminators KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-53 Subtypes: Exclusive or Inclusive If subtypes are exclusive, one supertype relates to at most one subtype. If subtypes are inclusive, one supertype can relate to one or more subtypes.

KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-54 Subtypes: Exclusive or Inclusive KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-55 Subtypes: Partial or Total

If a supertype has to be in one of the subtypes, then the subtypes are total subtypes. If a supertype does not have to be in any of the subtypes, then the subtypes are partial subtypes. KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-56 Subtypes: IS-A Relationships

Relationships connecting supertypes and subtypes are called IS-A relationships, because a subtype is a supertype. The identifier of the supertype and all of its subtypes must be identical; i.e., the identifier of the supertype becomes the identifier of the related subtype(s). Subtypes are used to avoid valueinappropriate nulls. KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-57

IE Crows Foot Symbol Summary I KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-58 IE Crows Foot Symbol Summary II KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-59

Strong Entity Patterns: 1:1 Strong Entity Relationships I KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-60 Strong Entity Patterns: 1:1 Strong Entity Relationships II KROENKE AND AUER - DATABASE PROCESSING, 14th Edition

2016 Pearson Education, Inc. 5-61 Strong Entity Patterns: 1:N Strong Entity Relationships I KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-62 Strong Entity Patterns:

1:N Strong Entity Relationships II KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-63 Strong Entity Patterns: 1:N Strong Entity Relationships III KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc.

5-64 Strong Entity Patterns: N:M Strong Entity Relationships I KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-65 Strong Entity Patterns: N:M Strong Entity Relationships II

KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-66 Strong Entity Patterns: N:M Strong Entity Relationships III KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-67

ID-Dependent Relationships: The Association Pattern I KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-68 ID-Dependent Relationships: The Association Pattern II PartNumber and CompanyName are the

identifiers of the parent entities, and are needed because QUOTATION is IDdependent on both PART and COMPANY An entity that holds one or more additional attributes beyond the identifiers of the parent entities is called an associative entity (or association entity)in this case Price is the additional attribute

KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-69 ID-Dependent Relationships: The Multivalued Attribute Pattern I KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-70

ID-Dependent Relationships: The Multivalued Attribute Pattern II KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-71 ID-Dependent Relationships: The Multivalued Attribute Pattern III KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc.

5-72 ID-Dependent Relationships: The Multivalued Attribute Pattern IV KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-73 ID-Dependent Relationships: Composite Multivalued Attributes I

KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-74 ID-Dependent Relationships: Composite Multivalued Attributes II KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-75

ID-Dependent Relationships: The Archetype/Instance Pattern I The archetype/instance pattern occurs when the ID-dependent child entity is the physical manifestation (instance) of an abstract or logical parent. PAINTING : PRINT CLASS : SECTION YACHT_DESIGN : YACHT HOUSE_MODEL: HOUSE KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc.

5-76 ID-Dependent Relationships: The Archetype/Instance Pattern II KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-77 ID-Dependent Relationships: The Archetype/Instance Pattern III

KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-78 Mixed Patterns: The Line-Item Pattern I KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-79

Mixed Patterns: The Line-Item Pattern II KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-80 Mixed Patterns: Other Mixed Patterns I Look for a mixed pattern where: A strong entity has a multivalued composite

group One of the elements of the composite group is an identifier of another strong entity. KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-81 Mixed Patterns: Other Mixed Patterns II IngredientName (which will

be added to INGREDIENT_USE as a foreign key in Chapter 6) is the identifier of a strong entity INGREDIENT_USE is a multivalued composite group holding the ingredient list data Amount and Instructions are ID-dependent on RECIPE

KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-82 Mixed Patterns: Other Mixed Patterns III Name (which will be added to EMPLOYEE_SKILL as a foreign key in Chapter 6) is the identifier of a strong entity

EMPLYEE_SKILL is a multivalued composite group holding the list of skills for each employee ProficiencyLevel and CourseTaken are IDdependent on EMPLOYEE KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-83 Mixed Patterns:

The For-Use-By Pattern I KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-84 Mixed Patterns: The For-Use-By Pattern II KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc.

5-85 Recursive Relationships A recursive relationship occurs when an entity has a relationship to itself. KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-86 Recursive Patterns: 1:1 Recursive Relationship

KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-87 Recursive Patterns: 1:N Recursive Relationship KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-88

Recursive Patterns: N:M Recursive Relationship III KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-89 Highline University Creating a Data Model Suppose the administration at a hypothetical university named Highline University wants to create a database

to track colleges, departments, faculty, and students. To do this, a data modeling team has collected a series of reports as part of its requirements determination. KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-90 Highline University The College Report KROENKE AND AUER - DATABASE PROCESSING, 14th Edition

2016 Pearson Education, Inc. 5-91 Highline University First Data Model KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-92 Highline University

The Department Report KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-93 Highline University Second Data Model I KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc.

5-94 Highline University Second Data Model II KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-95 Highline University Second Data Model III

KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-96 Highline University Second Data Model IV KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-97

Highline University The Department Student Report KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-98 Highline University Third Data Model KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc.

5-99 Highline University Sample Student Acceptance Letter KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-100 Highline University Fourth Data Model

KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-101 Highline University Final Data Model KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-102

David Kroenke and David Auer Database Processing Fundamentals, Design, and Implementation (14th Edition) End of Presentation: Chapter Five KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc.

5-103 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America. KROENKE AND AUER - DATABASE PROCESSING, 14th Edition 2016 Pearson Education, Inc. 5-104

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