Book. Title, Database design using entity-relationship diagrams. Edition, 2nd ed. Author(s), Bagui, Sikha ; Earp, Richard. Publication, Hoboken, NJ: CRC Press. Booktopia has Database Design Using Entity-Relationship Diagrams, Second Edition by Sikha Bagui. download a discounted PDF of Database. Conceptual Database Design With the ER Model Creating and Modifying Relations Using SQL Translating ER Diagrams with Aggregation Lecture slides for all chapters in MS Powerpoint, Postscript, and PDF formats. 2.
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Chapter 3 extends the one-entity diagram to include a second entity. The concept of database design using an entity relationship (ER) diagram. In designing a . Elmasri, R. and Navathe, S.B., Fundamentals of Database Systems, 3rd ed. Essential to database design, entity-relationship (ER) diagrams are known for their usefulness in Edition 2nd Edition Preview PDF. 3 Database Design Using Entity- Relationship Diagrams Sikha Bagui and Richard Earp for the ER Diagrams Defining a Second Entity Does a Relationship Exist? . Finally, we would like to thank Dr. Ed Rodgers, Chairman, Department of.
Yarberry, Jr. ISBN alk. Database design. Relational databases. Earp, Richard, II.
In practice, the scenario will be to produce as good an ER diagram as possible, map it to a relational model, and then shift the discussion to is this a good relational model or not? The approach to database design taken will be intuitive and informal. We do not deal with precise definitions of set relations.
The intent is to provide a mechanism to produce an ER diagram that can be presented to a user in English, and to polish the diagram into a specification that can then be mapped into a database. We then suggest testing the produced database by the theory of normal forms and other criteria i.
We also suggest a reverse-mapping paradigm for mapping a relational database back to an ER diagram for the purpose of documentation.
Why did we choose this model? Chen introduced the idea of ER diagrams Elmasri and Navathe, , and most database texts use some variant of the Chen model. Chen and others have improved the ER process over the years; and while there is no standard ER diagram ERD model, the Chen-like model and variants thereof are common, particularly in comprehensive database texts. There are also other reasons for choosing the Chen-like model over the other models.
With the Chen-like model, one need not consider how the database will be implemented.
The Barker-like model is more intimately tied to the relational database paradigm. Also, in the Barker-like and Oracle-like ERD, there is no accommodation for some of the features we present in the Chen-like model. For example, multi-valued attributes and weak entities are not part of the Barker- or Oracle-like design process.
It is not at all unusual to arrive at a design and then revise it. In developing ER models, one needs to realize that the Chen model is developed to be independent of implementation. The Chen-like model is used almost exclusively by universities in database instruction. The mapping rules of the Chen model to a relational database are relatively straightforward, but the model itself does not represent any particular logical model.
Elmasri, R. Property used to describe an entity or relationship. B Binary relationship: Relationship between two entities. C Candidate key: An attribute or set of attributes that uniquely identifies individual occurrences of an entity type. Cardinality ratio: Describes the number of one entity that is related to another entity. Composite attribute: An attribute composed of multiple components, each with an independent existence.
D Database: A shared collection of logically associated or related data. Degree of a relationship: The number of participating entities in a relationship. Derived attribute: An attribute that gets a value that is calculated or derived from the database. E Entity: Something in the real world that is of importance to a user and that needs to be represented in a database so that information about the entity can be recorded. An entity may have physical existence such as a student or building or it may have conceptual existence such as a course.
A collection of all entities of a particular entity type. Entity type: A set of entities of the same type. Where the domain of all attributes in a table must include only atomic simple, indivisible values, and the value of any attribute in a tuple or row must be a single-valued from the domain of that attribute.
Foreign Key: An attribute that is a primary key of another relation table. A foreign key is how relationships are implemented in relational databases. Full participation: Where all of one entity set participates in a relationship. Functional dependency: A relationship between two attributes in a relation. Attribute Y is functionally dependent on attribute X if attribute X identifies attribute Y. For every unique value of X, the same value of Y will always be found.
G Generalization: The process of minimizing the differences between entities by identifying their common features and removing the common features into a superclass entity. I Identifying owner: The strong entity upon which a weak entity is dependent. Identifying relationship: A weak relationship. K Key: An attribute or data item that uniquely identifies a record instance or tuple in a relation. M Mandatory relationship: Same as full participation; where all of one entity set participates in a relationship.
Where many tuples rows of one relation can be related to many tuples rows in another relation. Where many tuples rows of one relation can be related to one tuple row in another relation. The process of choosing a logical model and then moving to a physical database file system from a conceptual model the ER diagram. Multi-valued attribute: An attribute that may have multiple values for a single entity. A relationship where one tuple or row of one relation can be related to more than one tuple row in another relation.
A relationship where one tuple or row of one relation can be related to only one tuple row in another relation. Optional participation: A constraint that specifies whether the existence of an entity depends on its being related to another entity via a relationship type. P Partial key: The unique key in a dependent entity. Partial participation: Where part of one entity set participates in a relationship. Participation constraints also known as optionality: Determines whether all or some of an entity occurrence is related to another entity.
Primary key: A unique identifier for a row in a table in relational database; A selected candidate key of an entity. R Recursive relationship: Relationships among entities in the same class. Regular entity: See Entity. A table containing single-value entries and no duplicate rows. The meaning of the columns is the same in every row, and the order of the rows and columns is immaterial. Often, a relation is defined as a populated table.
An association between entities. S Second Normal Form: A relation that is in first normal form and in which each non-key attribute is fully, functionally dependent on the primary key.
Simple attribute: Attribute composed of a single value. The process of maximizing the differences between members of a superclass entity by identifying their distinguishing characteristics. Strong entity: An entity that is not dependent on another entity for its existence. Structural constraints: Indicate how many of one type of record is related to another and whether the record must have such a relationship.
The cardinality ratio and participation constraints, taken together, form the structural constraints. An entity type that has a distinct role and is also a member of a superclass. An entity type that includes distinct subclasses required to be represented in a data model.
Same as relation; a tabular view of data that may be used to hold one or more columns of data; an implementation of an entity. Third Normal Form: A relation that is in second normal form and in which no non-key attribute is functionally dependent on another non-key attribute i. U Unique identifier: W Waterfall model: A series of steps that software undergoes, from concept exploration through final retirement.
Weak entity: An entity that is dependent on some other entity for its existence. Chapter 5: Hoffer, Mary B. Prescott, Fred R. McFadden Robert C. Nickerson ISYS. How do we design the database for an application? Entity-Relationship Model Dr. Conceptual Design: Analyze the problem.
Identify the entities, relationships, and. Database Design Process there are six stages in the design of a database: Sabraz Nawaz M. Database Design Overview Conceptual Design.
Data Analysis 1 Unit 2. Entities, attributes,. A database is a collection of related data stored in a computer managed by a DBMS. Entity Relationship Model Disclaimer. Good Design 2. Functional Dependencies 3. Normalization Readings for this week: Quickstart, Ch. Complete the tutorial at http: Introduction to Computing Lectured by: Pham Tran Vu t.
Student Name: Wafa Ali Edrees Student Id: EER diagram is one of diagrams, which. Component 4: Introduction to Information and Computer Science Unit 6: The data model. Lecture ER Model is used at this stage. What are the entities. The Entity-Relationship Model After completing this chapter, you should be able to explain the three phases of database design, Why are multiple phases useful? Chapter 2: Entity-Relationship Model!
Entity Sets! Relationship Sets! Design Issues! Mapping Constraints! E-R Diagram! Extended E-R Features! Design of an E-R Database Schema! Reduction of an E-R.
Lesson 8: DBMS by Dr. The Entity-Relationship Model Steps in Database Design 1 Requirement Analysis Identify the data that needs to be stored data requirements Identify the operations that need to be executed on the data functional. It allows defining a representation of the real. Entity-Relationship Model Database Modeling Part 1 A conceptual data model, which is a representation of the structure of a database that is independent of the software that will be used to implement.
Data Modeling Basics Issued by: Sullivan, Ph. Database Design In database design, we determine: Vishnu Teja Saurabh Saxena Most the. Chapter 2 Hassan Khosravi Borrowing many slides from Rachel. Hoffer, University of Dayton Joey F. Review Quiz will contain very similar question as below. Some questions may even be repeated.
The order of the questions are random and are not in order of. Once the model is satisfactory, we then implement our design in a relational database. Database Fundamentals Robert J. Robbins Johns Hopkins University rrobbins gdb. A database is any collection of related data. A database. Ramakrishnan 1 Overview of Database Design Conceptual design: ER Model is used at.
Kim, Seattle University, bkim seattleu. Simsion and Graham C. N Spadaccini and W Liu Databases -. Lecture 2 Wael Aboulsaadat Acknowledgment: Ullman slides accompanying the course s textbook. Ramakrishnan and J. Gehrke 1 Today. Single-value constraints require that a.
Entity-Relationship Data Modeling: Tools and Techniques Copyright J.
Foundations of Computer Science Cengage Learning Objectives After studying this chapter, the student should be able to: Define a database and a database management system DBMS. Vladimir Zadorozhny vladimir sis. Chapter Simple information Database:. UNIT 1 1. Define Database? What is a DBMS? What is the need for database systems?
Define tupule? What are the responsibilities of DBA? Define schema?
Database Design 2: Design Methodology Objectives Discuss the general process and goals of database design Define user views and explain their function. Data Modeling Windows Enterprise Support Database Services provides the following documentation about relational database design, the relational database model, and relational database software.
Database Design Methodology Three phases Database Design Methodology Logical database Physical database Constructing a model of the information used in an enterprise on a specific data model but independent. Introduction The history of database system research is one of exceptional productivity and startling economic impact. We have learnt that from the days of file-based systems there are better ways to handle.
Introduction to Databases: Main Database. Design Method Objectives Discuss the general process and goals of database design Define user views and explain their function. Usually, there are two principles: Chapter 7.
Database Planning, Design and Administration Last few decades have seen proliferation of software applications, many requiring constant maintenance involving: Gehrke 1 Why Study the Relational Model? Most widely used model. Normalization Physical tuning 1 Problem: Analyze the requirements. Conceptually design the data e. Book Highlights This book focuses on presenting: 1 an ER design methodology for developing an ER diagram; 2 a grammar for the ER diagrams that can be presented back to the user; and 3 mapping rules to map the ER diagram to a relational database.
The steps for the ER design methodology, the grammar for the ER diagrams, as well as the mapping rules are developed and presented in a systematic, step-by-step manner throughout the book.
Also, several examples of sample data have been included with relational database mappings all to give a realistic feeling. This book is divided into ten chapters. The first chapter gives the reader some background by introducing some relational database concepts such as functional depen- 12 dencies and database normalization. The ER design methodology and mapping rules are presented, starting in Chapter 2.
Chapter 2 introduces the concepts of the entity, attributes, relationships, and the one-entity ER diagram. Steps 1, 2, and 3 of the ER Design Methodology are developed. The one-entity grammar and mapping rules for the one-entity diagram are presented.
Chapter 3 extends the one-entity diagram to include a second entity. The concept of testing attributes for entities is discussed and relationships between the entities are developed.
Steps 3a, 3b, 4, 5, and 6 of the ER design methodology are developed, and grammar for the ER diagrams developed up to this point is presented. Chapter 4 discusses structural constraints in relationships.
Several examples are given of , 1:M, and M:N relationships. Step 6 of the ER design methodology is revised and step 7 is developed.
A grammar for the structural constraints and the mapping rules is also presented. Chapter 5 develops the concept of the weak entity. This chapter revisits and revises steps 3 and 4 of the ER design methodology to include the weak entity. Again, a grammar and the mapping rules for the weak entity are presented. Chapter 6 discusses and extends different aspects of binary relationships in ER diagrams.
This chapter revises step 5 to include the concept of more than one relationship, and revises step 6 b to include derived and redundant relationships.
The concept of the recursive relationship is introduced in this chapter. The grammar and mapping rules for recursive relationships are presented. Chapter 7 discusses ternary and other higher-order relationships. Step 6 of the ER design methodology is again revised to include ternary and other, higher-order relationships. Several examples are given, and the grammar and mapping rules are developed and presented.
Chapter 8 discusses generalizations and specializations. Once again, step 6 of the ER design methodology is modified to include generalizations and specializations, and the grammar and mapping rules for generalizations and specializations are presented. Chapter 9 provides a summary of the mapping rules and reverse-engineering from a relational database to an ER diagram.
Chapters 2 through 9 present ER diagrams using a Chen-like model. Every chapter presents several examples.
Checkpoint sections within the chapters and end-of-chapter exercises are presented in every chapter to be worked out by the students to get a better understanding of the material within the respective sections and chapters. At the end of most chapters, there is a running case study with the solution i. Finally, we would like to thank Dr. Jim Bezdek, for encouraging us to complete this book. In designing a database with an ER diagram, we recognize that this is but one way to arrive at the objective the database.
There are other design methodologies that also produce databases, but an ER diagram is the most common. As we proceed through this material, we will occasionally point out where other models differ from the ER model. The ER model is one of the best-known tools for logical database design. Within the database community it is considered to be a very natural and easy-to-understand way of conceptualizing the structure of a database. In contrast, many educators have reported that students in database courses have difficulty grasping the concepts of the ER approach and, in particular, applying them to the real-world problems Goldstein and Storey, We took the approach of starting with an entity, and then developing from it in an inside-out strategy as mentioned in Elmasri and Navathe, Designing a software solution depends on correct elicitation.
In most software engineering paradigms, the process starts with a requirements elicitation, followed by a specification and then a feedback loop. In plain English, the idea is 1 tell me what you want requirements , and then 2 this is what I think you want specification. The process leads to an ER diagram that is then translated into plain but meant to be precise English that a user can understand. The iterative mechanism then takes over to arrive at a specification a revised ER diagram and English that both users and analysts understand.
The mapping of the ER diagram into a relational database is presented; mapping to other logical database models is not covered. We feel that the relational database is most appropriate to demonstrate mapping because it is the most-used contemporary database model. Actually, the idea behind the ER diagram is to produce a high-level database model that has no particular logical model implied relational, hierarchical, object oriented, or network.
We have a strong bias toward the relational model. The goodness of the final relational model is testable via the ideas of normal forms.
The goodness of the relational model produced by a mapping from an ER diagram theoretically should be guaranteed by the mapping process. If a diagram is good enough, then the mapping to a good relational model should happen almost automatically.
In practice, the scenario will be to produce as good an ER diagram as possible, map it to a relational model, and then shift the discussion to is this a good relational model or not? The approach to database design taken will be intuitive and informal. We do not deal with precise definitions of set relations. The intent is to provide a mechanism to produce an ER diagram that can be presented to a user in English, and to polish the diagram into a specification that can then be mapped into a database.
We then suggest testing the produced database by the theory of normal forms and other criteria i. We also suggest a reverse-mapping paradigm for mapping a relational database back to an ER diagram for the purpose of documentation. Why did we choose this model? Chen introduced the idea of ER diagrams Elmasri and Navathe, , and most database texts use some variant of the Chen model. Chen and others have improved the ER process over the years; and while there is no standard ER diagram ERD model, the Chen-like model and variants thereof are common, particularly in comprehensive database texts.
There are also other reasons for choosing the Chen-like model over the other models. With the Chen-like model, one need not consider how the database will be implemented.
The Barker-like model is more intimately tied to the relational database paradigm. Also, in the Barker-like and Oracle-like ERD, there is no accommodation for some of the features we present in the Chen-like model. For example, multi-valued attributes and weak entities are not part of the Barker- or Oracle-like design process. It is not at all unusual to arrive at a design and then revise it. In developing ER models, one needs to realize that the Chen model is developed to be independent of implementation.
The Chen-like model is used almost exclusively by universities in database instruction.