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Data Modeling and Database Design 2nd edition [Kõva köide]

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(University of Houston),
  • Formaat: Hardback, 720 pages, kõrgus x laius x paksus: 35x193x231 mm, kaal: 1292 g
  • Ilmumisaeg: 13-Jun-2014
  • Kirjastus: Course Technology Inc
  • ISBN-10: 1285085256
  • ISBN-13: 9781285085258
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Data Modeling and Database Design 2nd editionSuurem pilt
  • Formaat: Hardback, 720 pages, kõrgus x laius x paksus: 35x193x231 mm, kaal: 1292 g
  • Ilmumisaeg: 13-Jun-2014
  • Kirjastus: Course Technology Inc
  • ISBN-10: 1285085256
  • ISBN-13: 9781285085258
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781285085258.html
Märksõnad:
DATA MODELING AND DATABASE DESIGN presents a conceptually complete coverage of indispensable topics that each MIS student should learn if that student takes only one database course. Database design and data modeling encompass the minimal set of topics addressing the core competency of knowledge students should acquire in the database area. The text, rich examples, and figures work together to cover material with a depth and precision that is not available in more introductory database books.
Preface xvii
Chapter 1 Database Systems: Architecture and Components
1(29)
1.1 Data, Information, and Metadata
1(2)
1.2 Data Management
3(1)
1.3 Limitations of File-Processing Systems
3(3)
1.4 The ANSI/SPARC Three-Schema Architecture
6(4)
1.4.1 Data Independence Defined
8(2)
1.5 Characteristics of Database Systems
10(7)
1.5.1 What Is a Database System?
11(1)
1.5.2 What Is a Database Management System?
12(3)
1.5.3 Advantages of Database Systems
15(2)
1.6 Data Models
17(13)
1.6.1 Data Models and Database Design
17(2)
1.6.2 Data Modeling and Database Design in a Nutshell
19(6)
Chapter Summary
25(1)
Exercises
25(5)
Part I Conceptual Data Modeling
Chapter 2 Foundation Concepts
30(49)
2.1 A Conceptual Modeling Framework
30(1)
2.2 ER Modeling Primitives
30(2)
2.3 Foundations of the ER Modeling Grammar
32(47)
2.3.1 Entity Types and Attributes
32(3)
2.3.2 Entity and Attribute-Level Data Integrity Constraints
35(3)
2.3.3 Relationship Types
38(5)
2.3.4 Structural Constraints of a Relationship Type
43(9)
2.3.5 Base Entity Types and Weak Entity Types
52(5)
2.3.6 Cluster Entity Type: A Brief Introduction
57(1)
2.3.7 Specification of Deletion Constraints
58(12)
Chapter Summary
70(1)
Exercises
71(8)
Chapter 3 Entity-Relationship Modeling
79(62)
3.1 Bearcat Incorporated: A Case Study
79(2)
3.2 Applying the ER Modeling Grammar to the Conceptual Modeling Process
81(38)
3.2.1 The Presentation Layer ER Model
82(3)
3.2.2 The Presentation Layer ER Model for Bearcat Incorporated
85(19)
3.2.3 The Design-Specific ER Model
104(7)
3.2.4 The Decomposed Design-Specific ER Model
111(8)
3.3 Data Modeling Errors
119(22)
3.3.1 Vignette 1
120(7)
3.3.2 Vignette 2
127(7)
Chapter Summary
134(1)
Exercises
134(7)
Chapter 4 Enhanced Entity-Relationship (EER) Modeling
141(56)
4.1 Superclass/subclass Relationship
142(26)
4.1.1 A Motivating Exemplar
142(1)
4.1.2 Introduction to the Intra-Entity Class Relationship Type
143(2)
4.1.3 General Properties of a Superclass/subclass Relationship
145(1)
4.1.4 Specialization and Generalization
146(8)
4.1.5 Specialization Hierarchy and Specialization Lattice
154(3)
4.1.6 Categorization
157(3)
4.1.7 Choosing the Appropriate EER Construct
160(6)
4.1.8 Aggregation
166(2)
4.2 Converting from the Presentation Layer to a Design-Specific EER Diagram
168(2)
4.3 Bearcat Incorporated Data Requirements Revisited
170(1)
4.4 ER Model for the Revised Story
171(11)
4.5 Deletion Rules for Intra-Entity Class Relationships
182(15)
Chapter Summary
188(1)
Exercises
188(9)
Chapter 5 Modeling Complex Relationships
197(83)
5.1 The Ternary Relationship Type
198(7)
5.1.1 Vignette 1---Madeira College
198(5)
5.1.2 Vignette 2---Get Well Pharmacists, Inc
203(2)
5.2 Beyond the Ternary Relationship Type
205(19)
5.2.1 The Case for a Cluster Entity Type
205(1)
5.2.2 Vignette 3---More on Madeira College
206(6)
5.2.3 Vignette 4---A More Complex Entity Clustering
212(1)
5.2.4 Cluster Entity Type---Additional Examples
212(4)
5.2.5 Madeira College---The Rest of the Story
216(5)
5.2.6 Clustering a Recursive Relationship Type
221(3)
5.3 Inter-Relationship Integrity Constraint
224(6)
5.4 Composites of Weak Relationship Types
230(4)
5.4.1 Inclusion Dependency in Composite Relationship Types
230(1)
5.4.2 Exclusion Dependency in Composites of Weak Relationship Types
231(3)
5.5 Decomposition of Complex Relationship Constructs
234(12)
5.5.1 Decomposing Ternary and Higher-Order Relationship Types
234(1)
5.5.2 Decomposing a Relationship Type with a Multi-Valued Attribute
235(5)
5.5.3 Decomposing a Cluster Entity Type
240(1)
5.5.4 Decomposing Recursive Relationship Types
241(3)
5.5.5 Decomposing a Weak Relationship Type
244(2)
5.6 Validation of the Conceptual Design
246(11)
5.6.1 Fan Trap
246(5)
5.6.2 Chasm Trap
251(2)
5.6.3 Miscellaneous Semantic Traps
253(4)
5.7 Cougar Medical Associates
257(23)
5.7.1 Conceptual Model for CMA: The Genesis
259(6)
5.7.2 Conceptual Model for CMA: The Next Generation
265(1)
5.7.3 The Design-Specific ER Model for CMA: The Final Frontier
266(7)
Chapter Summary
273(1)
Exercises
273(7)
Part II Logical Data Modeling
Chapter 6 The Relational Data Model
280(78)
6.1 Definition
280(2)
6.2 Characteristics of a Relation
282(1)
6.3 Data Integrity Constraints
283(8)
6.3.1 The Concept of Unique Identifiers
284(6)
6.3.2 Referential Integrity Constraint in the Relational Data Model
290(1)
6.4 A Brief Introduction to Relational Algebra
291(5)
6.4.1 Unary Operations: Selection (α) and Projection (π)
292(1)
6.4.2 Binary Operations: Union (∪), Difference (-), and Intersection (∩)
293(2)
6.4.3 The Natural Join (*) Operation
295(1)
6.5 Views and Materialized Views in the Relational Data Model
296(1)
6.6 The Issue of Information Preservation
297(1)
6.7 Mapping an ER Model to a Logical Schema
298(22)
6.7.1 Information-Reducing Mapping of ER Constructs
298(17)
6.7.2 An Information-Preserving Mapping
315(5)
6.8 Mapping Enhanced ER Model Constructs to a Logical Schema
320(16)
6.8.1 Information-Reducing Mapping of EER Constructs
321(7)
6.8.2 Information-Preserving Grammar for Enhanced ER Modeling Constructs
328(8)
6.9 Mapping Complex ER Model Constructs to a Logical Schema
336(22)
Chapter Summary
345(2)
Exercises
347(11)
Part III Normalization
Chapter 7 Functional Dependencies
358(37)
7.1 A Motivating Exemplar
359(6)
7.2 Functional Dependencies
365(9)
7.2.1 Definition of Functional Dependency
365(1)
7.2.2 Inference Rules for Functional Dependencies
366(1)
7.2.3 Minimal Cover for a Set of Functional Dependencies
367(5)
7.2.4 Closure of a Set of Attributes
372(2)
7.2.5 When Do FDs Arise?
374(1)
7.3 Candidate Keys Revisited
374(21)
7.3.1 Deriving Candidate Key(s) by Synthesis
375(4)
7.3.2 Deriving Candidate Keys by Decomposition
379(3)
7.3.3 Deriving a Candidate Key---Another Example
382(4)
7.3.4 Prime and Non-prime Attributes
386(4)
Chapter Summary
390(1)
Exercises
390(5)
Chapter 8 Normal Forms Based on Functional Dependencies
395(72)
8.1 Normalization
395(25)
8.1.1 First Normal Form (1NF)
396(2)
8.1.2 Second Normal Form (2NF)
398(3)
8.1.3 Third Normal Form (3NF)
401(3)
8.1.4 Boyce-Codd Normal Form (BCNF)
404(3)
8.1.5 Side Effects of Normalization
407(11)
8.1.6 Summary Notes on Normal Forms
418(2)
8.2 The Motivating Exemplar Revisited
420(4)
8.3 A Comprehensive Approach to Normalization
424(18)
8.3.1 Case 1
424(7)
8.3.2 Case 2
431(5)
8.3.3 A Fast-Track Algorithm for a Non-Loss, Dependency-Preserving Solution
436(6)
8.4 Denormalization
442(1)
8.5 Role of Reverse Engineering in Data Modeling
443(24)
8.5.1 Reverse Engineering the Normalized Solution of Case 1
445(6)
8.5.2 Reverse Engineering the Normalized Solution of URS2 (Case 3)
451(2)
8.5.3 Reverse Engineering the Normalized Solution of URS3 (Case 2)
453(4)
Chapter Summary
457(1)
Exercises
458(9)
Chapter 9 Higher Normal Forms
467(39)
9.1 Multi-Valued Dependency
467(5)
9.1.1 A Motivating Exemplar for Multi-Valued Dependency
467(2)
9.1.2 Multi-Valued Dependency Defined
469(1)
9.1.3 Inference Rules for Multi-Valued Dependencies
470(2)
9.2 Fourth Normal Form (4NF)
472(4)
9.3 Resolution of a 4NF Violation---A Comprehensive Example
476(2)
9.4 Generality of Multi-Valued Dependencies and 4NF
478(2)
9.5 Join-Dependencies and Fifth Normal Form (5NF)
480(10)
9.6 A Thought-Provoking Exemplar
490(7)
9.7 A Note on Domain Key Normal Form (DK/NF)
497(9)
Chapter Summary
498(1)
Exercises
498(8)
Part IV Database Implementation Using the Relational Data Model
Chapter 10 Database Creation
506(33)
10.1 Data Definition Using SQL
507(17)
10.1.1 Base Table Specification in SQL/DDL
507(17)
10.2 Data Population Using SQL
524(15)
10.2.1 The INSERT Statement
525(3)
10.2.2 The DELETE Statement
528(2)
10.2.3 The UPDATE Statement
530(2)
Chapter Summary
532(1)
Exercises
532(7)
Chapter 11 Relational Algebra
539(28)
11.1 Unary Operators
542(4)
11.1.1 The Select Operator
542(2)
11.1.2 The Project Operator
544(2)
11.2 Binary Operators
546(21)
11.2.1 The Cartesian Product Operator
546(3)
11.2.2 Set Theoretic Operators
549(2)
11.2.3 Join Operators
551(6)
11.2.4 The Divide Operator
557(3)
11.2.5 Additional Relational Operators
560(3)
Chapter Summary
563(1)
Exercises
563(4)
Chapter 12 Structured Query Language (SQL)
567(68)
12.1 SQL Queries Based on a Single Table
569(28)
12.1.1 Examples of the Selection Operation
569(3)
12.1.2 Use of Comparison and Logical Operators
572(6)
12.1.3 Examples of the Projection Operation
578(2)
12.1.4 Grouping and Summarizing
580(3)
12.1.5 Handling Null Values
583(10)
12.1.6 Pattern Matching in SQL
593(4)
12.2 SQL Queries Based on Binary Operators
597(16)
12.2.1 The Cartesian Product Operation
597(2)
12.2.2 SQL Queries Involving Set Theoretic Operations
599(3)
12.2.3 Join Operations
602(6)
12.2.4 Outer Join Operations
608(4)
12.2.5 SQL and the Semi-Join and Semi-Minus Operations
612(1)
12.3 Subqueries
613(22)
12.3.1 Multiple-Row Uncorrelated Subqueries
613(12)
12.3.2 Multiple-Row Correlated Subqueries
625(3)
12.3.3 Aggregate Functions and Grouping
628(3)
Chapter Summary
631(1)
Exercises
631(4)
Chapter 13 Advanced Data Manipulation Using SQL
635(84)
13.1 Selected SQL:2003 Built-in Functions
635(16)
13.1.1 The SUBSTRING Function
636(3)
13.1.2 The CIIAR_LENGTH (char) Function
639(1)
13.1.3 The TRIM Function
640(3)
13.1.4 The TRANSLATE Function
643(1)
13.1.5 The POSITION Function
644(1)
13.1.6 Combining the INSTR and SUBSTR Functions
645(1)
13.1.7 The DECODE Function and the CASE Expression
646(3)
13.1.8 A Query to Simulate the Division Operation
649(2)
13.2 Some Brief Comments on Handling Dates and Times
651(5)
13.3 Hierarchical Queries
656(12)
13.3.1 Using the CONNECT BY and START WITH Clauses with the PRIOR Operator
658(2)
13.3.2 Using the LEVEL Pseudo-Column
660(1)
13.3.3 Formatting the Results from a Hierarchical Query
661(1)
13.3.4 Using a Subquery in a START WITH Clause
661(2)
13.3.5 The SYS_CONNECT_BY_PATH Function
663(1)
13.3.6 Joins in Hierarchical Queries
664(1)
13.3.7 Incorporating a Hierarchical Structure into a Table
665(3)
13.4 Extended GROUP BY Clauses
668(13)
13.4.1 The ROLLUP Operator
668(1)
13.4.2 Passing Multiple Columns to ROLLUP
669(2)
13.4.3 Changing the Position of Columns Passed to ROLLUP
671(1)
13.4.4 Using the CUBE Operator
672(2)
13.4.5 The GROUPING () Function
674(2)
13.4.6 The GROUPING SETS Extension to the GROUP BY Clause
676(1)
13.4.7 The GROUPING_ID ()
677(2)
13.4.8 Using a Column Multiple Times in a GROUP BY Clause
679(2)
13.5 Using the Analytical Functions
681(11)
13.5.1 Analytical Function Types
682(2)
13.5.2 The RANK () and DENSE_RANK () Functions
684(3)
13.5.3 Using ROLLUP, CUBE, and GROUPING SETS Operators with Analytical Functions
687(1)
13.5.4 Using the Window Functions
688(4)
13.6 A Quick Look at the MODEL Clause
692(8)
13.6.1 MODEL Clause Concepts
693(1)
13.6.2 Basic Syntax of the MODEL Clause
693(1)
13.6.3 An Example of the MODEL Clause
694(6)
13.7 A Potpourri of Other SQL Queries
700(19)
13.7.1 Concluding Example 1
700(2)
13.7.2 Concluding Example 2
702(2)
13.7.3 Concluding Example 3
704(1)
13.7.4 Concluding Example 4
704(1)
13.7.5 Concluding Example 5
705(1)
Chapter Summary
706(1)
Exercises
707(4)
SQL Project
711(8)
Appendix A Data Modeling Architectures Based on the Inverted Tree and Network Data Structures
719(12)
A.1 Logical Data Structures
719(3)
A.1.1 Inverted Tree Structure
719(2)
A.1.2 Network Data Structure
721(1)
A.2 Logical Data Model Architectures
722(9)
A.2.1 Hierarchical Data Model
722(4)
A.2.2 CODASYL Data Model
726(3)
Summary
729(1)
Selected Bibliography
729(2)
Appendix B Object-Oriented Data Modeling Architectures
731(8)
B.1 The Object-Oriented Data Model
731(6)
B.1.1 Overview of OO Concepts
732(3)
B.1.2 A Note on UML
735(2)
B.2 The Object-Relational Data Model
737(2)
Summary
738(1)
Selected Bibliography
738(1)
Selected Bibliography 739(4)
Index 743
Narayan S. Umanath is Professor [ Emeritus] of Information Systems at the University of Cincinnati (UC) in Cincinnati, Ohio, USA. He has been at UC since 1996 and served as the department head of Information Systems (IS) for 7 years. Before that, he has been a faculty member at the Pennsylvania State University at the main (University Park) campus and the University of Tulsa. Umanath also serves as a member of the faculty in the Department of Mathematics & Computer Science (DMACS) at the Sri Sathya Sai Institute of Higher Learning (SSSIHL) in Prashanti Nilayam, India in the capacity of Visiting Professor since 2003. Entering academia after 17 years of technical and managerial experience in software development, he received his Ph.D. in Business Administration from the University of Houston in 1987. His undergraduate and graduate educations are in mechanical engineering and industrial engineering, respectively.

Umanath has research publications in the domains of data visualization for decision support, agency theory applications in the design of compensation contracts, organizational computing, and electronic integration in supply channels; his current research interests are in the domains of knowledge exchange in supply-chain relationships, and data modeling & data warehousing. His research publications have appeared in Management Science, Decision Sciences, Journal of MIS, Communications of the ACM, International Journal of Information Management, Information & Management, Information Resources Management Journal, Journal of Managerial Issues and National Academy Science Letters. Umanath has also co-authored a text book titled "Data Modeling & Database Design". Richard W. Scamell serves as Associate Dean for Student Affairs and Professor of Decision and Information Sciences in the C. T. Bauer College of Business at the University of Houston. He received his Ph.D. degree from The University of Texas at Austin. Since joining the faculty at Houston in 1972, he has taught more than two dozen different courses at the undergraduate, masters, and doctoral levels, three of which have been focused in the database area. His publications have appeared in journals such as Management Science, MIS Quarterly, Academy of Management Journal, Decision Sciences, IEEE Transactions on Software Engineering, Communications of the ACM, Omega, and Information and Management.