Database Modeling and Design

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160 CHAPTER 8 Business Intelligence Shape Texture Size Win Date Promotion Estimating Detail «fk» shape id «fk» color id «fk» texture id «fk» density id «fk» size id «fk» estimate date id «dd» estimate number «fk» win date id «dd» job number «fk» customer id «fk» promotion id «fk» cost center id widget quantity estimated hours hourly rate estimated cost markup discount price Color Density Estimate Date Customer Cost Center Figure 8.6 Star schema for estimating process Dimension values are categorical in nature. For example, a given widget might have a density of fluffy or heavy. The values for the size dimension include small, medium, <strong>and</strong> large. Measures tend to be numeric, since they are typically aggregated using functions such as sum or average. The dimension tables should include any hierarchies that may be useful for analysis. For example, widgets are offered in many colors. The colors fall into categories by hue (e.g., pink, blue) <strong>and</strong> intensity (e.g., pastel, hot). Some even glow in the dark! The user may wish to examine all the pastel widgets as a group, or compare pink versus blue widgets. Including these attributes in the dimension table as shown in Figure 8.7 can accommodate this need. Color «pk» color id color description hue intensity glows in dark Figure 8.7 Color dimension showing attributes
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Database Modeling & Design Fourth E
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Joe Celko’s Data and Databases: C
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Publishing Director Michael Forster
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Contents Preface xv Chapter 1 Intro
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Contents xi 4.4.4 Merging and Restr
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Contents xiii Chapter 9 CASE Tools
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2 CHAPTER 1 Introduction In this ch
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4 CHAPTER 1 Introduction Informatio
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6 CHAPTER 1 Introduction II(b)], tw
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8 CHAPTER 1 Introduction tional rel
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10 CHAPTER 1 Introduction Customer
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2 The Entity-Relationship Model T h
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2.1 Fundamental ER Constructs 15 at
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2.1 Fundamental ER Constructs 17 Co
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2.1 Fundamental ER Constructs 19 12
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2.1 Fundamental ER Constructs 21 us
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2.2 Advanced ER Constructs 23 2.2 A
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2.2 Advanced ER Constructs 25 Softw
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2.2 Advanced ER Constructs 27 Proje
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2.2 Advanced ER Constructs 29 entit
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2.4 Literature Summary 31 2.4 Liter
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34 CHAPTER 3 The Unified Modeling L
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4 Requirements Analysis and Concept
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4.3 Conceptual Data Modeling 55 •
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4.3 Conceptual Data Modeling 57 inc
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4.3 Conceptual Data Modeling 59 Emp
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4.3 Conceptual Data Modeling 61 con
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4.3 Conceptual Data Modeling 63 Emp
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4.3 Conceptual Data Modeling 65 Div
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4.4 View Integration 67 Customer 1
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4.4 View Integration 69 nectivity c
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4.4 View Integration 71 Publication
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4.4 View Integration 73 N Publicati
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4.5 Entity Clustering for ER Models
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4.6 Summary 81 Department Division/
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5 Transforming the Conceptual Data
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5.1 Transformation Rules and SQL Co
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5.2 Transformation Steps 103 there
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5.2 Transformation Steps 105 5.2.3
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6 Normalization T his chapter focus
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6.1 Fundamentals of Normalization 1
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6.2 The Design of Normalized Tables
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6.3 Normalization of Candidate Tabl
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6.3 Normalization of Candidate Tabl
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6.4 Determining the Minimum Set of
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6.4 Determining the Minimum Set of
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6.5 Fourth and Fifth Normal Forms 1
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Page 296: 6.5 Fourth and Fifth Normal Forms 1
Page 300: 6.6 Summary 137 Table 6.8 Summary o
Page 304: 7 An Example of Logical Database De
Page 308: 7.2 Logical Design 141 7.2 Logical
Page 312: 7.2 Logical Design 143 create table
Page 316: 7.3 Summary 145 Table 7.3 Decomposi
Page 322: 148 CHAPTER 8 Business Intelligence
Page 348: 8.1 Data Warehousing 161 Date «pk
Page 352: 8.1 Data Warehousing 163 There are
Page 356: 8.1 Data Warehousing 165 Shape Text
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Page 384: 8.3 Data Mining 179 ments. Associat
Page 388: 8.3 Data Mining 181 Figure 8.18 Dou
Page 392: 8.3 Data Mining 183 Figure 8.21 Tri
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8.4 Summary 185 100 80 60 Cars Truc
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9 CASE Tools for Logical Database D
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9.1 Introduction to the CASE Tools
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9.2 Key Capabilities to Watch For 1
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9.3 The Basics 193 • An entity ta
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9.3 The Basics 195 Figure 9.5 Divis
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9.4 Generating a Database from a De
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9.5 Database Support 199 is massive
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9.7 Distributed Development 201 pro
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9.8 Application Life Cycle Tooling
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9.9 Design Compliance Checking 205
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9.11 Modeling a Data Warehouse 207
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9.12 Semi-Structured Data, XML 209
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9.13 Summary 211 9.13 Summary There
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214 Appendix: The Basics of SQL typ
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216 Appendix: The Basics of SQL cre
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218 Appendix: The Basics of SQL tab
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220 Appendix: The Basics of SQL Bas
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222 Appendix: The Basics of SQL tha
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224 Appendix: The Basics of SQL sel
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226 Appendix: The Basics of SQL (se
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228 Appendix: The Basics of SQL del
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Glossary activity diagram (UML)—A
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Glossary 233 database administrator
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Glossary 235 functional dependency
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Glossary 237 requirements specifica
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References Ambler, S. Agile Databas
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References 241 Cobb, R. E., Fry, J.
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References 243 Jajodia, S., and Ng,
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References 245 Sakai, H. “Entity-
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References 247 Zachmann, J. A. “A
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250 Exercises Assertions: • An in
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252 Exercises Transformation of the
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254 Exercises reservation_no -> dep
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256 Exercises 1. ABCD -> EFGHIJK 8.
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258 Exercises base schema. You shou
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260 Solutions to Selected Exercises
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262 Solutions to Selected Exercises
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264 About the Authors Tom Nadeau is
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266 Index Attributes (cont’d.) da
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268 Index Data manipulation languag
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270 Index Generalization, 23 comple
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272 Index Primary FDs (cont’d.) f
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274 Index Ternary relationships (co
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