The document discusses database concepts and categorizations. It defines single-user and multi-user databases based on the number of users, workgroup and enterprise databases based on user departments, centralized and distributed databases based on computer usage, and production and data warehouse databases based on data timeliness. Views can provide subsets of database information to simplify use or limit access. The relational model uses tables to integrate data while minimizing redundancy. E.F. Codd developed 12 rules for relational databases including presenting data in tables, unambiguous access, and physical and logical independence.

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Database
More database concepts and vocabulary

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Database Categorizations I
• How many users can use the database at a
given time?
– If one: It is a single-user database.
– If more than one: It is a multi-user database.
• If used by only several users in one department: It is
a workgroup database.
• If used by many departments across the entire
company: It is an enterprise database.

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Database Categorizations II
• How many computers are used for the
database? (Note that this is distinct from
how many computers do the users employ
to access the database.)
– If one: It is a centralized database.
– If more than one: It is a distributed database.
• There are various scenarios for handling distributed
databases, such as do the computers have distinct
data or copies of the same data, etc.

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Database Categorizations III
• How up-to-date is the data in the database?
– If the data is the database is used for the normal
daily operations, for example, what items are in
stock, what has been purchased in the present
quarter, etc.: It is a production database or
transaction database.
– If the data is more historical, for example, sales
over the last ten years, which one will look
back on to see trends, etc.: It is a data
warehouse database.

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Different Points of View
• One might think that integrating various
department’s data might
– Confuse the naïve user
– Provide the sophisticated user with too much information
(e.g. all the salary info, social security numbers, etc.)
• Views, however, can be used to provide a user with
only a subset of the database
– Simplifying life for the naïve user
– Providing a level of security by limiting what the
sophisticated user can access

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Database Design and Modeling
• The database approach was a big step forward
from the file-based approach.
• There have been various steps within the database
approach. One of the most important is the
development of the Relational Model.
• Models are simplified abstractions of real-world
events or conditions.
• Good models yield good database designs that are
the basis for good applications.

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Relational Model
• First developed by E. F. Codd.
• The main ingredient of the Relational Database
Management System is the “table” structure.
• A table is a matrix, consisting of a series of rows
and columns.
• One goal of the Relational model is to integrate a
lot of data, maintain the relationships within that
data, and yet minimize the amount of redundancy.

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E. F. Codd
Our Hero

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Relational Model
• Advantages
– Structural Independence
• Can change the database’s structure (e.g. add new
fields) without impairing the DBMS’s ability to
access and manipulate the data.
– Improved conceptual simplicity
– Facilitates implementation, management and
use
• Good design is crucial
– Ad hoc query capability
• Can answer questions that come up after the
database was designed.

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Basic Structure: Entities
• Entity
– A person, place or thing about which data are to be
collected and stored.
– Represented by a rectangle in the Entity-Relationship
(ER) diagram.
– Each entity is described by a set of attributes describing
a particular characteristic of the entity.
• Relationship
– An association among data. Most relationships
describe the associations between two entities.

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E. F. Codd’s Rules
• Rule 1: The Information Rule. All data
should be presented to the user in table
form.
• Rule 2: Guaranteed Access Rule. All data
should be accessible without ambiguity.
This can be accomplished through a
combination of the table name, primary key,
and column name.

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E. F. Codd’s Rules
• Rule 3: Systematic Treatment of Null Values. A
field should be allowed to remain empty. This
involves the support of a null value, which is
distinct from an empty string or a number with a
value of zero. Of course, this can't apply to
primary keys. In addition, most database
implementations support the concept of a non-
null field constraint that prevents null values in a
specific table column.

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E. F. Codd’s Rules
• Rule 4: Dynamic On-Line Catalog Based on the
Relational Model. A relational database must provide
access to its structure through the same tools that are used
to access the data. This is usually accomplished by storing
the structure definition within special system tables.
• Rule 5: Comprehensive Data Sublanguage Rule. The
database must support at least one clearly defined language
that includes functionality for data definition, data
manipulation, data integrity, and database transaction
control. All commercial relational databases use forms of
the standard SQL (Structured Query Language) as their
supported comprehensive language.

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E. F. Codd’s Rules
• Rule 6: View Updating Rule. Data can be presented to
the user in different logical combinations, called views.
Each view should support the same full range of data
manipulation that direct-access to a table has available. In
practice, providing update and delete access to logical
views is difficult and is not fully supported by any current
database.
• Rule 7: High-level Insert, Update, and Delete. Data can
be retrieved from a relational database in sets constructed
of data from multiple rows and/or multiple tables. This
rule states that insert, update, and delete operations should
be supported for any retrievable set rather than just for a
single row in a single table.

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E. F. Codd’s Rules
• Rule 8: Physical Data Independence. The user is isolated
from the physical method of storing and retrieving
information from the database. Changes can be made to the
underlying architecture (hardware, disk storage methods)
without affecting how the user accesses it.
• Rule 9: Logical Data Independence. How a user views
data should not change when the logical structure (tables
structure) of the database changes. This rule is particularly
difficult to satisfy. Most databases rely on strong ties
between the user view of the data and the actual structure
of the underlying tables.

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E. F. Codd’s Rules
• Rule 10: Integrity Independence. The database
language (like SQL) should support constraints on
user input that maintain database integrity. This
rule is not fully implemented by most major
vendors. At a minimum, all databases do preserve
two constraints through SQL. No component of a
primary key can have a null value. (see rule 3). If
a foreign key is defined in one table, any value in
it must exist as a primary key in another table.

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E. F. Codd’s Rules
• Rule 11: Distribution Independence. A user
should be totally unaware of whether or not the
database is distributed (whether parts of the
database exist in multiple locations).
• Rule 12: Nonsubversion Rule. There should be
no way to modify the database structure other than
through the multiple row database language (like
SQL). Most databases today support
administrative tools that allow some direct
manipulation of the datastructure.

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Field Versus Data
• In the Olympics database modeling homework, a
possible confusion is between a field/attribute and
data that might be entered into that field.
• For example, a confused designer might list figure
skating or alpine skiing as event fields. Rather
event fields should be something like
EventCategory and EventName. Then skating is
an example of an EventCategory and Women’s
Figure Skating is an example of an EventName.

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Entity Type/Occurrence
• Type versus occurrence
– The entity type/occurrence distinction is similar
to the class/object distinct in object-oriented
programming.
– An entity type is a template for an entity
occurrence.
• Dog is an entity type (class), whereas Lassie is an
entity occurrence (object).

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Entity Type/Occurrence
• The entity type (like a class) is a more
abstract gathering of associated data.
– E.g. Customer is an entity type that gathered
together properties such as FirstName,
LastName, etc.
• The entity occurrence (like an object) has
specific values
– E.g. John Smith is an entity occurrence with
FirstName of John, lastName of Smith, etc.

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When thinking of an entity type, think of
a table in design view.

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When thinking of an entity occurrence, think
about a specific row in DataSheet View

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Entities: Strong and Weak
• Entities are sometimes categorized as strong and
weak.
• A strong entity has an independent existence,
whereas the weak entity depends on some other
entity. The strong to weak relationship among
entities can be called
– Parent – child
– Owner – dependent
– Dominant – subordinate

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Strong-weak entity example
Event Trial
Strong
Parent
Owner
Dominant
Weak
Child
Dependent
Subordinate

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Relationship Type/Occurrence
• A relationship type is some association between
entity types. Like the entity type, the relationship
type is an abstract template.
– E.g. “Places” is a relationship type between the
Customer and Order entity types.
• A relationship occurrence is a specific
association between specific entity occurrences.
– E.g. John Smith placed Order ORD0004 is a
relationship occurrence.

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When thinking of a relationship type,
think of the lookup wizard in design

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When thinking of a relationship occurrence,
think of choosing a foreign key from a drop-
down list

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Express Relationships as Verbs
• Relationships are generally expressed as verbs.
For example,
– Athlete comes from Country
– Athlete competes in Event
• The relationship can be represented by a line
between the two rectangles representing the
participating entities. The verb is written on the
line. In the Chen model, the verb is placed in a
diamond.

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Chen modeled relationship
Athlete
AthleteID
AthleteFName
…
Event
EventID
EventCategory
…
Competes in

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Basic Structure: Relationships
• Relationships are said to have a
multiplicity.
• Relationships are categorized by how many things are
related to how many things.
– 1:M (one-to-many)
– M:N (many-to-many)
– 1:1 (one-to-one)

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Relationship Examples
• One-to-many
– A country will be represented by many athletes, but
each athlete represents only one country.
• Many-to-many
– An athlete may compete in many events, and an event
has many athletes competing in it.
• One-to-one
– Each country has one athlete serve as flag bearer in the
opening ceremony.

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Degree of a Relationship
• Relationships are said to have a degree (the
number of entity types involved).
– Binary: involves two entities
– Ternary: involves three entities
– Quaternary: involves four entities
• Even if not using the Chen model, ternary
and higher degree relationships are
represented using a diamond.

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Ternary Relationship Example
StockHolder StockBroker
Stock
Buy/Sell
A StockHolder buys or
sells a stock through a
StockBroker.
No arrows in
relationships
with degree
higher than 2.

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Quaternary Relationship Example
Make a movieActor Director
Producer
Writer

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Recursive Relationship
• If an entity (type) has a relationship with
itself, that relationship is called recursive.
• The entity occurrences in the relationship
may be distinct.
• Since the subject and object are of the same
entity type, the “roles” the occurrences are
playing in the relationship may be added to
the diagram.

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Recursive Relationship
• A typical example here is if one employee
serves as the supervisor of another
employee.
– While the relationship involves different entity
occurrences (i.e. two different employees), it
involves only one entity type. Thus as far as
entity type goes, the Employee entity has a
relationship with itself.

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Role names are used to clarify situations
with multiple relationships
Faculty Student
Teaches 
Advises 
Teacher Student
Advisor Advisee

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Attributes
• Attributes are the properties of an entity.
– E.g. the attributes of a Customer are FirstName,
LastName, etc.
• Relationships can also have properties.
– E.g. a stock is bought or sold on a particular
date (at a particular price).
– (One may consider introducing a new entity
called a transaction.)

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Attribute Domain
• An attribute’s domain is the set of values that a
property is allowed to take on.
• For example,
– The quantity of items ordered is ≥ 0
– The price paid is ≥ 0
– Gender would be ‘M’ or ‘F’ (or perhaps NULL)
– Phone numbers consist of numbers only. One can also
specify the number of digits or a range thereof.

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Self-documenting attribute names
• When it comes time to implement the database
and one is turning attributes into the
corresponding fields, resist the temptation to use
abbreviated field names.
• If you use descriptive field names, your
implementation will be self-documenting – in that
many people will know what you mean simply by
the name you have used.
– Some designers suggest that the first part of a field
name refer to the table/entity it belongs to. This can be
especially useful with common fields like IDs and
names.

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Attributes: Simple or Composite
• A property that takes on a value that cannot
be broken down into pieces is called simple,
(aka “atomic”)
– E.g. quantity, price, gender
• A property that can be broken into
constituent properties is called composite.
– E.g. address → street, city, state, zipcode
– name → firstName, lastName

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Attributes: Single-valued or Multi-valued
• Single-valued: a property that takes on only one
value at a time for a given entity occurrence.
– E.g. dateOfBirth, you only have one
• Multi-valued: a property that can take on more
than one value at the same time for a given entity
occurrence
– E.g. phoneNumber (home and cell number)
– E.g. beneficiary

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Attributes: Derived
• If a property can be determined from other
properties, it is said to be derived.
– E.g. age or AgeCategory (20-29, 30-39, etc.)
can be derived from dateOfBirth
– E.g. taxBracket can be derived from
grossIncome and deductions
– E.g. city might be derivable from zipcode

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Keys
• A candidate key is a minimal set of
properties that uniquely determine each
entity occurrence (record, row, tuple).
• A candidate key may be composite, i.e.
consisting of more than one property.
• Minimal above means that if one property is
removed from the set, the set no longer
uniquely determines an occurrence.

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Keys (Cont.)
• There can be more than one candidate key.
– In the school’s database (banner), a person can be
identified by his or her
• socialSecNumber
• idNumber
• pidm
• The primary key is the candidate key that is
selected to identify the entity occurrence
internally (within the database).
• A candidate not chosen to be the primary is
sometimes called an alternate key.

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References
• Database Systems Rob and Coronel
• Database Systems, Connolly and Begg
• SQL for Dummies, Taylor
• http://www.metacard.com/wp1a.html
• http://www.oracle.com/glossary/index.html?axx.html
• http://www.itworld.com/nl/db_mgr/05072001/
• Concepts of Database Management, Pratt and
Adamski