1. Chapter 17 Designing Databases
Systems Analysis and Design
Kendall and Kendall
Fifth Edition

2. Copyright © 2002 by Prentice Hall, Inc.
Major Topics
Files
Databases
Normalization
Key design
Using the database
Data warehouses
Data mining
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3. Data Storage Design Objectives
Data must be available when the user wants it
Data must have integrity -- accurate & consistent
Efficient data storage, maintenance and retrieval
Information retrieval be purposeful
Information obtained must be useful for
Managing
Planning
Controlling
Decision making
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4. Data Storage Approaches
Store data in individual files; each unique to a particular application.
Can be designed and built quite rapidly.
Concerns for data availability and security are minimized
Choose an appropriate file structure according to the system requirements
Store data in a database.
A formally defined and centrally controlled store of data intended for use in many different applications
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5. Objectives of Effective Databases
Ensure data can be shared among users for a variety of applications
Maintain data that are both accurate & consistent
Ensure all data required for current and future applications will be readily available
Allow the database to evolve
Allow users to construct their personal view of the data without concern for the way the data are physically stored
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6. Efficiency Measures of Database Design
Time
Cost for
design & development
operation and maintenance
hardware installation
user training
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Entities
A distinct collection of data for one person, place, thing, or event
Entities become files of database tables
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Attributive Entity
describes attributes, especially repeating elements
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Associative Entity
The relationship line between a many-to-many relationship becomes an associative entity.
Sometimes called a composite entity or gerund
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10. Associative Entity Connections
Links two entities
Each entity end has a “one” connection
The associative entity has a “many” connection on each side
Can only exist between two entities
Become database tables
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11. Key of an Associative Entity
The primary key for each “one” end is a foreign key on the associative entity
Both foreign keys concatenated together become the primary key
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Relationships
Associations between entities may be
One-to-one
One-to-many
Many-to-many
A single vertical line represents one
A circle represents zero or none
A crows foot represents many
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Relationships
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Ordinality
The ordinality is the minimum number that can occur in a relationship
If the ordinality is zero, it means that it is possible to have none of the entity
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Entity Subtype
A special one-to-one relationship
It is used to represent additional attributes, which may not be present on every record of the first entity
This eliminates null fields on the primary database
For example, a company that has preferred customers, or student interns
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Self-Join
A self-join is when a record has a relationship with another record on the same file
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17. Attributes, Records, and Keys
Attributes are a characteristic of an entity, sometimes called a field
Records are a collection of data items that have something in common
Keys are data items in a record used to identify the record
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Key Types
Primary key, unique for the record
Secondary key, may not be unique
Concatenated key, a combination of two or more data items for the key
Foreign key, a data item in one record that is the key of another record
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Types of Files
Master files
Have large records
Contain all pertinent info. about an entity
Transaction files
Are short records
Contain info. used to update master files
Table file
Work file
Report file
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File Organization
Sequential organization
Linked lists
Hashed file organization
Indexed organization
Indexed-sequential organization
VSAM (Virtual Storage Access Method), sequential and indexed-sequential files
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Databases
Intended to be shared by users
Three types of databases:
Hierarchical databases
Network databases
Relational databases
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Normalization
Transformation of complex user views and data to a set of smaller, stable, and easily maintainable data structures
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Normalization
Creates data that are stored only once on a file with the exception of key fields
This eliminates redundant data storage
It provides ideal data storage for database systems
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24. Three Steps of Data Normalization
Remove all repeating groups and identify the primary key
Ensure that all nonkey attributes are fully dependent on the primary key
Remove any transitive dependencies, attributes which are dependent on other nonkey attributes
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Normalization
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Data Model Diagrams
Used to show relationships between attributes
An oval represents an attribute
A single arrow line represents one
A double arrow line represents many
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First Normal Form (1NF)
Remove any repeating groups
All repeating groups are moved into a new table
Foreign keys are used to link the tables
When a relation contains no repeating groups, it is in 1NF
Keys must be included to link the relations, tables
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28. Second Normal Form (2NF)
Remove any partial dependencies
A partial dependency is when the data are only dependent on a part of a key field
A relation is created for the data that are only dependent on part of the key and another for data that are dependent on both parts
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Third Normal Form (3NF)
Remove any transitive dependencies
A transitive dependency is when a relation contains data that are not part of the entity
The problem with transitive dependencies is updating the data
A single data item may be present on many records
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30. Entity-Relationship Diagram (ERDs) and Record Keys
When the relationship is one-to-many, the primary key of the file at the one end of the relationship should be contained as a foreign key on the file at the many end of the relationship
A many-to-many relationship should be divided into two one-to-many relationships with an associative entity in the middle
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31. Guidelines for Creating Master Files or Database Relations
Each separate entity should have it's own master file or database relation
A specific, nonkey data field should exist on only one master file or relation
Each master file or relation should have programs to create, read, update, and delete records
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32. Integrity Constraints
Help ensure that the database contains accurate data:
Three Types:
Entity integrity constraints, which govern the composition of primary keys
Referential integrity, which governs the denature of records in a one-to-many relationship
Domain integrity
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Entity Integrity
The primary key cannot have a null value
If the primary key is a composite key, none of the fields in the key can contain a null value
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34. Referential Integrity
Referential integrity means that all foreign keys in one table (the child table) must have a matching record in the parent table
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35. Referential Integrity
You cannot add a record without a matching foreign key record
You cannot change a primary key that has matching child table records
A child table that has a foreign key for a different record
You cannot delete a record that has child records
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36. Referential Integrity
A restricted database updates or deletes a key only if there are no matching child records
A cascaded database will delete or update all child records when a parent record is deleted or changed
The parent triggers the changes
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Domain Integrity
Domain integrity defines rules that ensure that only valid data are stored on database records
Domain integrity has two forms:
Check constraints, which are defined at the table level
Rules, which are defined as separate objects and may be used within a number of fields
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38. Guidelines to retrieve and present data
Choose a relation from the database
Join two relations together
Project columns from the relation
Select rows from the relation
Derive new attributes
Index or sort rows
Calculate totals and performance measures
Present data
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Denormalization
Process of taking the logical data model and transforming it into an efficient physical model
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Data Warehouses
Used to organize information for quick and effective queries
Data is organized around major subjects
Data stored as summarized rather than detailed raw data
Data covers a much longer time frame than in a traditional transaction-oriented database
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Data Warehouses
Organized for fast queries
Usually optimized for answering complex queries, known as OLAP
Allow for easy access via data-mining software called siftware
Include data from multiple databases that has been “cleaned”
Usually contains data from outside sources (“overlays”)
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42. Online Analytic Processing (OLAP)
Meant to answer decision makers’ complex questions by defining a multidimensional database
Data mining, or knowledge data discovery (KDD), is the process of identifying patterns that a human is incapable of detecting
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43. Data Mining Decision Aids
Statistical analysis
Decision trees
Neural networks
Fuzzy logic
Data visualization
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Data Mining Patterns
Associations, patterns that occur together
Sequences, patterns of actions that take place over a period of time
Clustering, patterns that develop among groups of people
Trends, the patterns that are noticed over a period of time
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