Post on 13-Dec-2015
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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David M. Kroenke’s
Chapter Four:
Database Design
Using Normalization
Database Processing:Fundamentals, Design, and Implementation
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
4-2
Chapter Premise
• Received one or more tables of existing data
• Need to store data in new database
• Problem: Store data as received, or transform?
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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How Many Tables?
One, two, more?
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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Assessing Table Structure
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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Counting Rows in a Table
• To count the number of rows in a table use the SQL built-in function COUNT(*):
SELECT COUNT(*) AS NumRows FROM SKU_DATA;
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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Examining the Columns
• To determine the number and type of columns in a table, use an SQL SELECT statement
• To limit the number of rows retreived, use the SQL TOP {NumberOfRows} keyword:
SELECT TOP (10) *
FROM SKU_DATA;
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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Checking Validity of Assumed Referential Integrity Constraints
• Given two tables with an assumed foreign key constraint:
SKU_DATA (SKU, SKU_Description, Department, Buyer)
BUYER (BuyerName, Department)
Where SKU_DATA.Buyer must exist in BUYER.BuyerName
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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Checking Validity of Assumed Referential Integrity Constraints
• To find any foreign key values that violate the foreign key constraint:SELECT BuyerFROM SKU_DATAWHERE Buyer NOT IT
(SELECT BuyerFROM SKU_DATA, BUYERWHERE SKU_DATA.BUYER =
BUYER.BuyerName;
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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Type of Database
• Updateable
• Read-only
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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Updateable Database
• Normally, put tables in BCNF
• Always, remove MVD
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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Read-Only Database
• Likely not to use BCNF tables
• Remove MVD
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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Designing Updateable Databases
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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Normalization:Advantages and Disadvantages
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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Choosing Not to Use BCNF
• BCNF controls anomalies from functional dependencies
• BCNF is not, always, desirable
• Classic ZIP code example
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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Multivaled Dependencies
• MVDs cause very problematic anomalies
• Always place MVDs into a separate tables for operational databases.
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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Designing Read-Only Databases
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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Read-Only Database
• non-operational database
• data extracted from operational databases
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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Read-Only Databases
• For applications– querying – reporting – data mining
• NEVER updated operationally
(new data imported)
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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Read-Only Databases
• Data Warehouse
• Data Mart
• Decision Support System
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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Read-Only Databases
• Normalization seldom an advantage
• Denormalization – Join data
• from normalized tables • To create a new table
– Store • New non-normalized tables• Not original normalized tables
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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Read-Only Databases
• Often, many copies of the same data
• Each customized for a specific application
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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Customized Tables
PRODUCT_PURCHASING (SKU, SKU_Description, VendorNumber, VendorName, VendorContact_1, VendorContact_2, VendorStreet, VendorCity, VendorState, VendorZip)
PRODUCT_USAGE (SKU, SKU_Description, QuantitySoldPastYear, QuantitySoldPastQuarter, QuantitySoldPastMonth)
PRODUCT_WEB (SKU, DetailPicture, ThumbnailPicture, MarketingShortDescription, MarketingLongDescription, PartColor)
PRODUCT_INVENTORY (SKU, PartNumber, SKU_Description, UnitsCode, BinNumber, ProductionKeyCode)
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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Creating Databases fromExisting Tables
• Generally, accomplished by automated means– Import– SQL
• Beyond the scope of this class
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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Common Design Problems
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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The Multivalue, Multicolumn Problem
• WORKER (WorkerID, Name, Skill-Type, BuildID1, BuildID2, BuildID3)
• Problems?
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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The Multivalue, Multicolumn Problem
• WORKER (WorkerID, Name, Skill-Type, BuildID1, BuildID2, BuildID3)
• Another form of a MVD
• A Different Solution: – use separate table to store multiple values– WORKER (WorkerID, Name, Skill-Type)
– ASSIGNMENT (WorkerID, BuildingID
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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Inconsistent Values: Different forms of the same data value
• Different codings:• CommonName = ‘Blueberry' • CommonName = ‘Huckleberry'• CommonName = ‘Wild Blueberry‘
• Different spellings:• ‘Blueberry’ • ‘Blueberries’ • ‘BLUBERRY’
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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Inconsistent Values
• Particularly problematic are – primary key values,or
– foreign key values
• To detect:– Some semi-automatic methods
– Beyond the scope of this class
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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Null Values
• Missing value
• Not a blank
• Not a zero
• Never been provided!
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
4-30
Null Values
• Ambiguous:– May indicate n/a– May indicate applicable but unknown– May indicate value applicable and known, but
never entered
• When is your DB fault intolerant of null values?
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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The General-Purpose Remarks Column
• Black Hole– Remarks– Comments– Notes
• Such a column may:– Be used inconsistently– Hold multiple data items
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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The General-Purpose Remarks Column
• Example – Figure 4-10, p. 118
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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The General-Purpose Remarks Column
• Decompose column into separate columns
• Manual task
DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall, modified by Dr. Lyn Mathis
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David M. Kroenke’s Database Processing
Fundamentals, Design, and Implementation
(10th Edition)
End of Presentation:Chapter Four